Datasets:

common-pile
/

pressbooks

Dataset card Data Studio Files Files and versions Community

Split (1)

train · 107k rows

id stringlengths 40 265	text stringlengths 209 2.18M	source stringclasses 1 value	added stringdate 2025-03-22 05:08:50 2025-03-22 06:09:39	created stringclasses 651 values	metadata dict
https://ecampusontario.pressbooks.pub/oerbysubjectpeerguide/chapter/__unknown__/	\| Credibility \| Relevance \| \| Authorship \| Publisher, Year of Update \| Title of Open Textbook \| Aim of Open Textbook Identified by Author(s) \| Intended Audience \| Topical Foci \| \| Anderson, R., RN, BScN, MN, Associate Teaching Professor, Thompson Rivers University, Kamloops Doyle, R. G., MSN, BSN, Faculty, British Columbia Institute of Technology, Burnaby McCutcheon, J. A., BSN, Nursing Instructor, British Columbia Institute of Technology, Burnaby \| Thompson Rivers University, 2022 \| Clinical Procedures for Safer Patient Care \| Aims to provide standardized processes for clinical skills and to help nursing schools and clinical practice partners keep procedural practice current using a checklist approach. Each skill/ procedure has learning outcomes, a brief overview of the relevant theory, checklists of steps for procedures with the rationale behind each step of the process, and a summary of key takeaways. \| Nursing students and practice partners \| - Infection Control - Patient Assessment - Safe Patient Handling, Positioning, & Transfers - Wound Care - Oxygen Therapy - Non-Parenteral & Parenteral Medication Administration - Intravenous Therapy - Blood Glucose Monitoring - Tubes & Attachments \| \| Belcik, K., PhD, RN, CNE, Clinical Associate Professor, Texas State University, San Marcos Open Resources for Nursing \| Texas State University Libraries, 2023 \| Leadership and Management of Nursing Care \| Introduces concepts related to nursing leadership and management, prioritization strategies, delegation and supervision, legal implications of nursing practice, ethical nursing practice, collaboration within the interprofessional team, health care economics, quality and evidence-based practice, advocacy, preparation for the RN role, and the avoidance of burnout with self-care. \| Nursing students (BScN) \| - Overview of Management and Professional Issues - Delegation and Supervision - Leadership and Management - Legal Implications - Ethical Practice - Collaboration Within the Interprofessional Team - Health Care Economics - Quality and Evidence-Based Practice - Advocacy \| \| Buccieri, K., PhD, Associate Professor, Sociology; Coordinator of Criminology, Trent University, Peterborough Davy, J., Harm Reduction Specialist, Community-based HIV/STBBI Programs Support. Prevention. Education, Peterborough Gilmer, C., RN, HlthSc, Director of Department of Social Work & Associate Professor, School of Nursing, Trent University, Peterborough Whitmore, N., Outreach & Addictions Worker, Port Hope & Cobourg \| eCampus Ontario, 2022 \| Understanding Homelessness in Canada \| Explores homelessness in Canada, from a range of different perspective inclusive of lived experience, and academic viewpoints. Seemingly simple questions are posed through the use of real-life scenarios, embedded interview videos, artwork, and interactive activities, demonstrate how the answers are actually rather complex. \| Health Science undergraduates \| - Introduction to Homelessness - Indigenous and Canadian studies [Indigenous Studies; Politics, Policy & Housing in Canada] - Mental Health & Public Health Studies - Population Studies [Gender & Queer Studies; Child & Youth Studies] - Social Sciences [Sociology & Crimino-legal Studies; Social Work] - Health Sciences [Primary Care & Nursing; Emergency Medicine] \| \| Bush, J., Ph.D., RN, CNE, Lecturer, University of West Florida, Pensacola Der Like, J. V., DNP, MSN, RNC, Clinical Assistant Professor, University of West Florida, Pensacola \| University of West Florida, 2023 \| Mental Health is a Verb \| Reviews the foundations of mental health, psychopharmacology, and the most common mental health diagnoses in a pocket guide format. \| Nursing students \| - Foundational Concepts in Mental - Health Nursing [Mental Health versus Mental Illness; Theoretical Models; Clinical Assessment, Problems, & Treatment; Psychopharmacology; Mental Health Promotion; Legal & Ethical Issues; Therapeutic Communication] - Mental Disorders [Depression, Anxiety, Bipolar; Somatic Symptom; Eating; Substance Abuse & Addiction; Schizophrenia; Personality; Childhood; Psychiatric Emergencies; Grief & Loss] \| \| Carter, K., B.Ed., M.A., Open Educational Resource Consultant, Conestoga College, Kitchener Rutherford, M. Dip., Nursing, LD., BGS., Faculty Supports, Georgian College, Barrie \| eCampus Ontario, 2022 \| Building a Medical Terminology Foundation \| Breaks down medical terms into their word parts, pronounces medical terms, and learn the meaning of medical terms within the context of introductory anatomy and physiology. \| Health science students \| - Identifying Word Parts in Medical Terms - Medical Language Rules - Prefix - Suffix - Medical Language within the Context of Anatomy & Physiology [15 systems such as integumentary, respiratory, digestive] \| \| Chippewa Valley College, Eau Claire Egert, A., MSN, RN, CCSNE, CCNE, CCCI, Faculty, British Columbia Institute of Technology, Burnaby Lee, K., MSN, RN, CCNE, Faculty, British Columbia Institute of Technology, Burnaby Gill, M., MSN, RN, Manager, Academic Planning and Quality Assurance, British Columbia Institute of Technology, Burnaby \| BCcampus, 2023 \| Fundamentals of Nursing Pharmacology: A Conceptual Approach \| Explores pharmacological concepts, showing the connections between pathophysiology, pharmacological principles, and common medication classes using a concept-based teaching approach \| Nursing students (BScN) \| - Principles of Pharmacology - Safety & Ethics - Antimicrobials - Autonomic Nervous System Regulation - Gas Exchange - Perfusion & Renal Elimination - Gastrointestinal Elimination - Central Nervous System Regulation, Mood, & Cognition - Endocrine - Pain & Mobility \| \| Christman, E., MSN, RN, CNE, CHSE, APNP-BC, Nursing Faculty, Southern New Hampshire University, Manchester Ernstmeyer, K., MSN, RN, CNE, CHSE, APNP-BC, Open RN Grant Project Director, Chippewa Valley Technical College, Eau Claire \| Wisconsin Technical College Digital Press, 2022 \| Nursing Management and Professional Concepts \| Introduces concepts related to nursing leadership and management, prioritization strategies, delegation and supervision, legal implications of nursing practice, ethical nursing practice, collaboration within the interprofessional team, health care economics, quality and evidence-based practice, advocacy, preparation for the RN role, and the avoidance of burnout with self-care. \| Nursing students \| - Overview of Management & Professional Issues - Prioritization - Delegation & Supervision - Leadership & Management - Legal Implications - Ethical Practice - Collaboration within the Interprofessional Team - Health Care Economics - Quality and Evidence-based Practice - Advocacy - Preparation for the RN Role - Burnout & Self-care \| \| Christman, E., MSN, RN, CNE, CHSE, APNP-BC, Nursing Faculty, Southern New Hampshire University, Manchester Ernstmeyer, K., MSN, RN, CNE, CHSE, APNP-BC, Open RN Grant Project Director, Chippewa Valley Technical College, Eau Claire \| Wisconsin Technical College Digital Press, 2022 \| Mental Health and Community Concepts \| Discusses mental health and community health concepts with emphasis on stress management techniques, healthy coping strategies, referrals to community resources, and other preventative interventions. Examines nursing care for individuals with specific mental health and substance use disorders and the nurse’s role in community health needs assessments and caring for vulnerable populations \| Nursing students \| - Foundational Mental Health Concepts - Therapeutic Communication & the Nurse-Client Relationship - Stress, Coping, & Crisis Intervention - Application of the Nursing Process to Mental Health Care - Legal & Ethical Considerations in Mental Health Care - Psychotropic Medications - Disorders [Depressive, Bipolar, Anxiety, Personality, Psychosis & Schizophrenia; Childhood & Adolescence, Eating, Substance Use] - Trauma, Abuse & Violence - Community Assessment - Vulnerable Populations - Environmental Health & Emergency Preparedness \| \| Daphne Cockwell School of Nursing \| Toronto Metropolitan University, 2023 \| The Daphne Cockwell School of Nursing – Clinical Practice and Simulation Resource Toronto Metropolitan University \| Provides learning materials to support nursing education, clinical practice, and simulation. \| Nursing students \| - Virtual Simulations [Emergency; Medical Surgical; Pediatrics; Maternal Newborn; Gerontology; Mental Health; Rehabilitation; Home Health Care] - Open Educational Resources [Communication & Documentation; Health Assessment; Immunizations; Infection Prevention and Control; Nutrition; Professional Practice in Nursing; Scholarly Writing] - Actor and Mannequin Simulation [Discharge Teaching on Cast Care; Newborn Assessment; Older Adult with Infection; MAID] - Open Educational Resources \| \| D’Ilio, A., RN, BSN, MSN, Nursing Instructor, British Columbia Institute of Technology, Burnaby Egert, A., MSN, RN, CCSNE, CCNE, CCCI, Faculty, British Columbia Institute of Technology, Burnaby \| BCcampus, 2023 \| Nursing Virtual Case Studies \| Aims to develop noticing and clinical judgment skills through case study approach. Each case study is designed to work with a specific level of learner (novice to advanced) and uses a variety of resources to help learners prepare for the experience and reflect on their actions. \| Nursing students \| - Case 1: Care of an Older Adult with Impaired Perfusion in a Community Setting - Case 2: Care of an Older Adult with Infection and Impaired Cognition in an Acute Medical Setting - Case 3: Care of a Young Adult with Diabetes, Altered Mood, and Substance Use in a Community Setting - Case 4: Care of the an Adult Experiencing Post-Operative Complications in an Acute Surgical Setting \| \| Ernstmeyer, K., MSN, RN, CNE, CHSE, APNP-BC, Open RN Grant Project Director, Chippewa Valley Technical College, Eau Claire Elizabeth, C., DNP, RN, CNE, Open RN Grant Project Director and Nurse Educator, Chippewa Valley Technical College, Eau Claire \| University of Regina, 2021 \| Nursing Skills \| Encourages engagement in critical thinking and apply the nursing process while carrying out assessments and foundational nursing skills. \| Nursing students (BScN and PN) \| - General Survey Assessment - Health History - Areas of Assessment [Neurological, Head & Neck, Eye & Ear; Cardiovascular; Respiratory; Abdominal; Musculoskeletal] - Skills [Blood Pressure; Aseptic Technique; Math Calculations; - Oxygen Therapy; Administration of medications; Enteral Tube Management; Specimen Collection; Wound Care; Facilitation of Elimination; Tracheostomy care & Suctioning; IV Therapy Management] \| \| Exner-Pirot, H., PhD, Political Science, Macdonald-Laurier Institute, Ottawa Norbye, B., Professor Nursing and Health Care Education, Arctic University of Norway, Tromsø Butler, L., RN, PHD, Dean, Faculty of Nursing, Dalhousie University, Halifax \| University of Regina, 2021 \| Northern and Indigenous Health and Healthcare \| Provides accessible content on health and health care from a northern perspective for the growing number of health professionals being educated in northern communities. It entails the provision of northern health care in the context of unique challenges and circumstances. \| Nursing students (BScN) \| - Community Health in Northern & Indigenous Communities [Oral Health & Health Promotion; Food Security; Water Poverty; Indigenous Birth; Working with Unbounded Communities; Everyday Life with Chronic Illness; Role of Nurses in Preventing Tuberculosis; Community Development] - Social Determinants and Structural Impacts on Northern & Indigenous Health [Sexual Violence; Sexual Abuse; Childhood Sexual Abuse; Culture of Violence and Silence; Family Health & Local Capacity Building] - Culture & Health [Infant Feeding Teachings; Indigenous Sami Adolescents’ Well-being and Mental Health; Northern & Indigenous Ethical Imperative for Working with Communities; Establishing Trustful Relationships between Marginalized Indigenous People & Researchers/Health Workers] - Health care innovations [Telehealth, Social Media, e-Health; Telemedicine] - Professional Practice [Nursing Leadership; Meaning of being a nurse in Canada’s Northern Territories; Professionalism & Professional Conduct; Self-care Practices of Practitioners; Nurse-Deaconess Education; Educating Health Care Providers; Off-campus Nurse Education; Health Research & Education] \| Hillary, C., RN, BSN, MHSED, Instructor, Selkirk College, Castlegar Girvin, J., RN, MN, Instructor, College of the Rockies, Cranbrook Gaubreau, S., RN, MSN-Ed, Faculty Assistant, Selkirk College, Castlegar Bird, K., RN, BScN, MEd, Instructor, Selkirk College, Castlegar \| BCcampus, 2022 \| BC Schools of Nursing Virtual Simulation Games \| Not available. \| Nursing students \| - Post-Operative Care of a Client who Identifies as Non-Binary \| \| Hughes, M., MEd, BScN, RN, Professor, School of Community and Health Studies, Centennial College, Toronto Stefopulos, C., MEd, RN, Professor, Centennial College, Toronto Doyle, S., BScN student, Toronto Metropolitan University, Centennial, George Brown Collaborative Nursing Degree Program, Toronto Metropolitan University, Toronto \| eCampus Ontario, 2022 \| An Interprofessional Virtual Gaming Simulation: Breaking the Chain of Transmission \| Promotes the application of knowledge and skills related to interprofessional collaboration, and infection prevention and control practices within virtual simulation \| Nursing students \| - An Interprofessional Virtual Gamily Simulation: Breaking the Chain of Transmission - Interprofessional Healthcare Provider Role Descriptions \| \| Lapum, J., PhD, MN, BScN, RN, Associate Director, Quality Assurance and Professor, Toronto Metropolitan University, Toronto St-Amant, O., BScN, MScN, PhD, Assistant Professor, Toronto Metropolitan University, Toronto Hughes, M., MEd, BScN, RN, Professor, School of Community and Health Studies, Centennial College, Toronto Petrie, P., RN, BScN, MScN, Professor, George Brown College, Toronto Morrell, S., RN(EC), BScN, MN, PhD(c), Lecturer, Faculty of Nursing, University of Windsor, Nurse Practitioner, Windsor Regional Hospital, Windsor Mistry, S., RM, BHScM, BScN student, Toronto Metropolitan University, Centennial, George Brown Collaborative Nursing Degree Program, Toronto Metropolitan University, Toronto \| eCampus Ontario, 2022 \| The Complete Subjective Health Assessment \| Deconstructs the categories of the complete subjective health assessment, with explanations and examples of what constitutes relevant subjective data. Provides an opportunity to learn how to respond to normal, abnormal, and critical findings when completing a complete subjective health assessment. \| Nursing students \| - Introduction to the Complete Subjective Health Assessment [Subjective Health Assessment; Reasons for Conduction; Categories & Influencing Factors] - The Complete Subjective Health Assessment [ Demographic & Biographic Data; Main Health Needs; PQRSTU Assessment; Current & Past Health; Mental Health & Mental Illnesses; Functional Health; Preventative Treatments & Examinations; Family Health; Cultural Health] - Cultural Safety & Care Partners \| \| Lapum, J., PhD, MN, BScN, RN, Associate Director, Quality Assurance and Professor, Toronto Metropolitan University, Toronto Ronquillo, C. MSN, RN, Assistant Professor, Toronto Metropolitan University, Toronto Hughes, M. MEd, BScN, RN, Professor, School of Community and Health Studies, Centennial College, Toronto Garmaise-Yee, J., DNP, MN, RN, Professor, George Brown College, Toronto \| Toronto Metropolitan University, 2022 \| Introduction to Communication in Nursing \| Guides best practices in communication in the context of the nursing profession including communication theory, therapeutic communication and interviewing, and interprofessional communication. \| Nursing students (BScN) \| - Introduction to Communication and Communication Theory in Nursing [Professional Communication; Positive Lens of Conflict; Conflict Resolution; Social Media; Models of Communication; Communication is Learned; Theoretical Approaches to Communication (Trauma-informed; Relational; Anti-racist)] - Therapeutic Communication & Interviewing [CNO Standard Statement; Client Interview; Preparing for the Interview; Self-reflection prior to the Interview; Beginning the Interview; Types of Interview Questions; Communication Strategies; Non-verbal Communication Strategies; Developmental Considerations] - Interprofessional Communication in Nursing [Interpersonal; Interprofessional; Conflicts; Harassment & Communication; Resources to Facilitate Interprofessional Communication; Self-Reflection in Interprofessional Communication] \| \| Lapum, J., PhD, MN, BScN, RN, Professor, Toronto Metropolitan University, Toronto St-Amant, O., PhD, MN, BScN, RN, Assistant Professor, Toronto Metropolitan University, Toronto Hughes, M., MEd, BScN, RN, Professor, Centennial College, Toronto Tan, A., BScN, RN, University Health Network, Toronto Bogdan, A., BScN, RN Frances, D., BScN student, Toronto Metropolitan University, Centennial, George Brown Collaborative Nursing Degree Program, Toronto Metropolitan University, Toronto Frantzke, R, BScN student, Toronto Metropolitan University, Centennial, George Brown Collaborative Nursing Degree Program, Toronto Metropolitan University, Toronto Savicevic, N., MA Interactive Design, MArch, BScEng, Educational Developer, Toronto Metropolitan University, Toronto \| Toronto Metropolitan University, 2022 \| The Scholarship of Writing in Nursing: 1st Canadian Edition \| Guides best practices of scholarly writing in the context of the nursing profession. \| Nursing students (undergraduates and graduates) and Health Science students \| - Introduction [Scholarly writing; Relevance of Scholarly Writing] - Reading & Comprehension [Reading Effectively; Best place & Time for Reading; Distracted Reader; Learning Preferences, Types of text; Pre-reading Strategies; Reading the Article in Full; Notetaking; Annotating; Dialectic Notetaking; Reading Critically; Critiquing a Text; Reflecting & Summarizing; Troubleshooting your Reading] - Information literacy [Finding Texts; Researching the Topic; Search Operators; Evaluating Quality; Peer-reviewed Sources; Popular Sources & Grey Literature; Caution: Websites] - Types of Writing [Descriptive, Reflective, Analytical; Persuasive, Critical; Editorial; Literature Review] - Fundamentals of Writing [Audience; Purpose; Point of View; Tone; Originality; Sentence & Paragraph Construction; Clear, Complex, Respectful, & Inclusive Language; Grammar & Style Tips] - Writing Process & Pre-Writing [Starting Point; Brainstorming; Thesis; Organizing & Connecting Ideas; Developing an Outline] - Drafting & Writing a paper [First Draft; Good vs weak Support; Transitions; Patterns of Organization; Writing Introductions & Conclusions] - Revising & Editing a paper [Revise to improve Organization, Cohesion, Style & Tone; Editing for Clarity & Conciseness; Giving & Receiving Feedback; Creating & Refining a Title; Revising & Editing Checklist] - Academic integrity & APA Style Rules [Plagiarism & Self-Plagiarism; Paraphrasing & Direct Quotations; Citing another person’s Ideas; In-text Citation types; Reference Page Examples \| \| Lapum, J., PhD, MN, BScN, RN, Associate Director, Quality Assurance and Professor, Toronto Metropolitan University, Toronto St-Amant, O., BScN, MScN, PhD, Assistant Professor, Toronto Metropolitan University, Toronto Hughes, M., MEd, BScN, RN, Professor, School of Community and Health Studies, Centennial College, Toronto Ronquillo, C., MSN, RN, Assistant Professor, Toronto Metropolitan University, Toronto Hughes, M., MEd, BScN, RN, Professor, School of Community and Health Studies, Centennial College, Toronto Garmaise-Yee, J., DNP, MN, RN, Professor, George Brown College, Toronto \| Toronto Metropolitan University, 2020 \| Documentation in Nursing: 1st Canadian edition \| Guides best practices of documentation in the nursing profession by addressing principles of documentation, legislation associated with documentation, methods and systems of documentation, and key trends in the future of documentation. Incorporates legislation and practice standards specific to the province of Ontario, Canada. \| Nursing students (BScN) \| - Introduction to Documentation - Reasons for Documentation - Documentation Components - Electronic Documentation Systems - Privacy of Documentation - Privacy, Confidentiality, and Security - Methods of Documentation - Methods of Documentation – Examples - Future of Documentation \| \| Lapum, J., PhD, MN, BScN, RN, Associate Director, Quality Assurance and Professor, Toronto Metropolitan University, Toronto Hughes, M., MEd, BScN, RN, Professor, School of Community and Health Studies, Centennial College, Toronto \| Toronto Metropolitan University, 2021 \| Introduction to Health Assessment for the Nursing Professional \| Offers an inclusive approach to health assessment that incorporates culturally responsive techniques related to race/ethnicity, gender/sex/sexual orientation, body sizes/types, and ability/disability. It is the first health assessment resource that is informed by clinical judgment with the goal to facilitate students’ clinical decision making and ability to prioritize care by recognizing and acting on cues and signs of clinical deterioration. \| Nursing students \| - Introduction to Health Assessment [Related Legislation; Clinical Judgement; Priorities of Care; Guiding Approaches; Health Promotion (behavioral, relational, structural); Health Determinants] - Inclusive Approaches to Health Assessment [Inclusive Assessment; Moving Beyond Culture & Ant-Oppression in Health Assessment; Inclusive Health Assessment with Indigenous, Black & LGBTQ12SA+] - Respiratory System - Cardiovascular System - Gastrointestinal System \| \| Lapum, J., PhD, MN, BScN, RN, Associate Director, Quality Assurance and Professor, Toronto Metropolitan University, Toronto Verkuyl, M., NP PHC, MN, Professor, Centennial College, Toronto Garcia, W., MS, BScN, RN, Instructor, Toronto Metropolitan University, Toronto St-Amant, O., BScN, MScN, PhD, Assistant Professor, Toronto Metropolitan University, Toronto Tan, A., BScN, RN, University Health Network, Toronto \| BCcampus, 2021 \| Vital Sign Measurement Across the Lifespan – 2nd Canadian Edition \| Provides opportunities to read about, observe, practice, and test vital sign measurement using a multi-media and interactive approach, it will provide. \| Nursing and Health Science students \| - Introduction - Temperature - Pulse & Respiration - Oxygen Saturation - Blood Pressure - Knowledge Integration [Case Studies across the Lifespan] \| \| Rees, G., RN, MSN, ACHIP, FAMIA, Program Lead Digital Health Advanced Certificate, British Columbia Institute of Technology, Burnaby Kruger, R., RN, Med, CNCC(C), Technology & Equipment Coordinator, British Columbia Institute of Technology, Burnaby Morrison, M., PhD, MA (Ed.), BHSc. (N), RN, Program Head, British Columbia Institute of Technology, Burnaby \| BCcampus, 2022 \| Health Case Studies: Toward Closing the Healthcare Communication Gap \| Provides eight separate acute health case studies. Each case study includes the patient narrative or story that models the best practice in healthcare settings. Associated with each case is a set of specific learning objectives to support learning and facilitate educational strategies and evaluation. \| Nursing students (BScN) \| - Chronic Obstructive Pulmonary - Disease - Pneumonia - Unstable Angina - Health Failure - Motor Vehicle Collison - Sepsis - Deep Vein Thrombosis \| \| St-Amant, O., BScN, MScN, PhD, Assistant Professor, Toronto Metropolitan University, Toronto Dybenko, L., RN, BEd, MSc(A), PhD, George Brown College, Toronto Reisdorfer, E. RN, PhD, Assistant Professor, MacEwan University, Edmonton Scott, L., RN, PhD, Professor, Toronto Metropolitan University, Centennial, George Brown Collaborative Nursing Program, Toronto Jennings, A. MN, PhD, Professor George Brown College, Toronto Haghiri-Vijeh, R. RN, BN, MN, PhD, Professor, Centenial College, Toronto \| Toronto Metropolitan University, 2022 \| Professional Practice in Nursing 201: Supplementary \| Not available. \| Nursing students \| - 11 case studies involving clients and families living with chronic illness across the life-cycle \| \| Trip, K., Nurse Practitioner, Assistant Professor, University of Toronto, Toronto Wilson, J., Nurse Practitioner, Assistant Professor, University of Toronto, Toronto \| eCampus Ontario, 2022 \| Gynecological Assessment \| Provides four online gynecological assessment learning modules in combination with virtual knowledge checks that will help articulate an approach, demonstrate techniques of examination, identify and diagnose disorders, and develop an evidence-based plan. \| Nursing students (BScN) \| - Gynecological History - Gynecological Exam - Additional Considerations [Pelvic Exam – Hesitancy, Anxiety, History of Trauma, Differently Abled; 2SLGBTQI+] - Common Gynecological Disorders: Masses & Viral Lesions, Dermatosis, Infections] \| \| Wagner, J., PhD, RN, Retired Assistant Professor, University of Regina, Regina \| University of Regina Press, 2022 \| Leadership and Influencing Change in Nursing \| Provides foundation tools, techniques, and knowledge required to empower students to meet the leadership challenges found within the incessantly changing health care environment. \| Nursing students (BScN) \| - Leadership Strengths & Growth Opportunities - Diversity in Health Care Organizations - Working with Indigenous Leadership & Indigenous Environments - Role of Nurse Leaders in Development of the Canadian Health Care System - Primary Health Care - Leadership in Quality Management & Safety - Leaders & Evidence-informed Decision Making - Change theories - Using Advocacy to Galvanize Ethics into Action - Managing Conflict - Responsibility & Authority of Leaders - Emergency Preparedness & Response - Leadership through Informatics - Regulation, the law, Labor Relations, and Negotiations - Emerging Leadership Issues \| \| Woodend, K. RN, MSc, PhD, Associate Professor, Trent University, Peterborough Lemonde, M., RN, PhD, Associate Professor, Ontario Tech University, Oshawa McCabe, J., RN, MEd, PhD, Associate Professor, Ontario Tech University, Oshawa Thibeault, C., RN, PhD, Associate Professor, Trent University, Peterborough \| eCampus Ontario, 2022 \| Leadership for Nurses in Clinical Settings \| Develops and enhances knowledge about the science and art of leadership \| Nursing students (graduate) \| - Power & Politics [Leadership Theories & Styles; Poser & Poser Dynamics] - Communication & Delegation [Principles of Effective Communication; Personal Leadership Style; Strategies for Effective Leading; Barriers to Communication; Steps to Effective Delegation] - Ethical & Legal Issues [Ethics Foundations; Ethics and the Law; Ethics Framework; Plan of Care; Living at Risk & Moral Distress] - Quality Management & Improvement [Historical Overview; Quality & Patient Safety; Team Huddles; Programs] - Change Management [Key Drivers of an Organization’s Culture; Change Management Theory; Organizational Readiness; Role of Leaders; Challenges & Best Practice] - Health Informatics [Theoretical, Social, and Legal Issues; Informatics in Patient Care Settings; Health Data Storage & Exchange; Telemedicine] - Budgeting & Resource Management [Budget Process; Developing a Budget; Monitoring & Analyzing a Budget] - Recruiting, selecting, & retaining staff [Frameworks and Guidelines; Targeting Populations] - Managing teams/conflict [Complex Process; Potential Causes of Organization Conflict; Nursing & Patient Care Conflicts; Strategies to Resolve Conflict; Restoring & Remaining Trust] - Managing, Motivating & Staff Development [Setting the Stage; Supporting Professional Development; Performance Management] - Risk Management & Emergency preparedness [Preparedness; Response; Recovery & Resilience] - Ethical Leadership & Building Leadership Career [Fostering Ethical Practice; Values in Leadership Practice; Courage & Moral Resilience] \| \| \| \| \| \| \|	pressbooks	2025-03-22T05:08:50.984510	09-30-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/oerbysubjectpeerguide/chapter/__unknown__/", "book_url": "https://ecampusontario.pressbooks.pub/oerbysubjectpeerguide/front-matter/introduction/", "title": "2023 Compendium of North American Nursing OER", "author": "Kyle Montgomery, Marnie Seal, Shannon Dowdall-Smith, Remar Mangaoil, Phyllis Montgomery, Sharolyn Mossey", "institution": "Cambrian College, Laurentian University", "subject": "Nursing, Nursing fundamentals and skills, Nursing specialties, Nursing management and leadership" }
https://ecampusontario.pressbooks.pub/oerbysubjectpeerguide/chapter/accessibilityinteractivityandlicensing/	2 Accessibility/Interactivity and Licensing \| Title of Open Textbook \| Accessibility/Interactivity \| Licensing \| \|\|\| \| Figures and tables \| a \| Review questions \| Total interactive H5P activities \| Available for use with \| \| \| ✓ \| ✓ \| ✓ \| 33 activities – \| \|\| \| ✓ \| \| \| \| \|\| \| ✓ \| ✓ \| ✓ \| 92 activities – \| \|\| \| ✓ \| ✓ \| \| 10 activities – image slider, image hotspots, multiple choice, drag the words, crossword \| \|\| \| ✓ \| ✓ \| ✓ \| 146 activities – \| \|\| \| ✓ \| ✓ \| ✓ \| 30 activities – \| \|\| \| ✓ \| ✓ \| \| 37 activities – dialog cards, question set, fill in the blanks, drag text, crossword, \| \|\| \| ✓ \| \| \| 50 activities – \| \|\| \| \| ✓ \| \| 2 activities – image hotspot \| \|\| \| ✓ \| ✓ \| ✓ \| 13 activities – \| \|\| \| ✓ \| ✓ \| ✓ \| 86 activities – \| \|\| \| Northern and Indigenous Health and Healthcare \| ✓ \| \| \| \| \| \| ✓ \| \| ✓ \| 2 activities – \| ✓ (CC BY-NC-SA 4.0) \| \| \| An Interprofessional Virtual Gaming Simulation: Breaking the Chain of Transmission \| ✓ \| ✓ \| ✓ \| 34 activities – \| \| \| ✓ \| ✓ \| ✓ \| 11 activities – question set, multiple choice \| \|\| \| Introduction to Communication in Nursing \| ✓ \| \| \| 29 activities –multiple choice, drag text, \| \| \| ✓ \| ✓ \| \| 94 H5P activities – fill in the blanks, drag text, true/false \| \|\| \| ✓ \| \| \| 12 activities – multiple choice, drag text \| \|\| \| Introduction to Health Assessment for the Nursing Professional \| ✓ \| ✓ \| ✓ \| 55 H5P activities – multiple choice, fill in the blanks, course presentation \| \| \| Vital Sign Measurement Across the Lifespan – 2nd Canadian Edition \| ✓ \| ✓ \| \| 122 activities – \| \| \| Health Case Studies: Toward Closing the Healthcare Communication Gap \| \| \| ✓ \| \| \| \| \| \| ✓ \| \| \|\| \| ✓ \| \| \| 8 activities – question sets \| \|\| \| ✓ \| ✓ \| ✓ \| \| \|\| \| ✓ \| ✓ \| \| 71 activities – \| \|\|	pressbooks	2025-03-22T05:08:51.036889	09-30-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/oerbysubjectpeerguide/chapter/accessibilityinteractivityandlicensing/", "book_url": "https://ecampusontario.pressbooks.pub/oerbysubjectpeerguide/front-matter/introduction/", "title": "2023 Compendium of North American Nursing OER", "author": "Kyle Montgomery, Marnie Seal, Shannon Dowdall-Smith, Remar Mangaoil, Phyllis Montgomery, Sharolyn Mossey", "institution": "Cambrian College, Laurentian University", "subject": "Nursing, Nursing fundamentals and skills, Nursing specialties, Nursing management and leadership" }
https://openoregon.pressbooks.pub/esol23/chapter/chapter-1-2/	Grammar – Beginning Videos and Exercises Greetings Greetings – Watch the videos and complete the exercises. Exercise 1: Greetings Read and complete the conversations below. Type words in the blank boxes. Exercise 2: Greetings Click on the eight pictures and words below. Four pictures match four words. Find the pairs. Exercise 3: Greetings Extra Read and complete the conversations below. Type words in the blank boxes. Greetings Useful Expressions Flip Cards Use the cards below to study the words: read, repeat, write, say, listen. Listen to the words and read the words. This exercise does not work well with screen readers.	pressbooks	2025-03-22T05:08:51.054092	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/chapter-1-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/28/	Grammar – Beginning Videos and Exercises Alphabet Alphabet – Watch the videos and complete the exercises. Exercise 1: Alphabet Exercise Put the upper case letters of the alphabet in order. Drag the letters to the correct drop zone boxes below. Exercise 2: Alphabet Exercise Put the lower case letters of the alphabet in alphabetical order. Drag the letters to the correct drop zone boxes below. Exercise 3: Alphabet Exercise Click on each of the sixteen squares below. Match the upper case letter with the lower case letter. Exercise 4: Alphabet Exercise There are twenty-six names below. Put the names in alphabetical order. Drag and drop the names to the drop zone boxes below.	pressbooks	2025-03-22T05:08:51.074435	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/28/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/3/	Grammar – Beginning Videos and Exercises Counting Counting – Watch the videos and complete the exercises. Exercise 1: Numerical Order: Put the following twenty numbers in numerical order. Number one and number twenty are already placed. Drag the other numbers to the drop zone boxes below. Exercise 2: Numerical Order Drag the words to the correct drop zone boxes. Put number words in the correct numerical order. Exercise 3: Numbers and Words Click on each of the twenty-two squares below. Match the numbers with the word for the number. Make a pair. Exercise 4: Numbers, Words, and Pictures Click on each of the twenty-two squares below. Match the numbers with the word for the number. Make a pair.	pressbooks	2025-03-22T05:08:51.095133	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/3/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/4/	Grammar – Beginning Videos and Exercises Be and Subject Pronouns Subject Pronouns – Watch the video and complete the exercises Exercise 1: Be Verb Read the following sentences. Type the correct form of the “Be” verb in the blank boxes. Exercise 2: Subject Pronouns Click on the fourteen squares below. Find the picture and the subject pronoun that match. Exercise 3: Be and Subject Pronoun Read the sentences. Click on one of the three words in the brackets to complete the sentence. Click the check button. Exercise 4: Pronoun and Be Verb Form Choose the correct form of the verb for each pronoun. Click on one of the three choices in the brackets following the pronoun. Click the check button.	pressbooks	2025-03-22T05:08:51.111047	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/4/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/5/	Grammar – Beginning Videos and Exercises Be and Questions Be and Questions – Watch the video and complete the exercises. Exercise 1: Be Verb Questions Click on one of the three words in brackets to complete the question. Click the check button. Exercise 2: Be Questions and Answers Read the questions below. Answer the questions. Drag best answer to the drop zone boxes following each question. Exercise 3: Be Negative Questions and Answers Read the questions below. Drag the answers to the drop zone boxes following the questions. Exercise 4: Be Verb Forms Read the sentences. Drag the correct word to the drop zone boxes to complete the sentences.	pressbooks	2025-03-22T05:08:51.124896	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/5/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/6/	Grammar – Beginning Videos and Exercises Have Have – Watch the video and complete the exercises. Exercise 1: To Have – Verb Complete the sentences. Type words in the boxes to complete each sentence. Exercise 2: To Have – Negative Type “don’t have” or “doesn’t have” in the blank boxes below to complete the following sentences. Exercise 3: Have – Practice Read the sentences. Click on one of the words between the brackets to complete the sentence. Click the check button. Exercise 4: Have and Have Not Type the correct forms of “have” or “do” in the blank boxes below.	pressbooks	2025-03-22T05:08:51.140077	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/6/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/7/	Grammar – Beginning Videos and Exercises Have – Questions and Colors Have with Questions and Colors – Watch the videos and complete the exercises. Exercise 1: To Have Questions and Answers Answer the questions below. Drag the answers to the drop zone boxes following each question. Exercise 2: Colors Click on the eighteen squares below. Match the picture and the word. Exercise 3: To be, To have, Colors Read the following story. Type a form of “be”, “have”, or a “color word” for each blank box in the story. Exercise 4: Review Read the story. Type the correct form of “be” or “have” in the blank boxes.	pressbooks	2025-03-22T05:08:51.155073	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/7/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/8/	Grammar – Beginning Videos and Exercises Time and Dates Time and Dates – Watch the videos and complete the exercises. Exercise 1: Time and Dates Matching Click on the twenty-six squares below. Match the numerical time or date with the image on a clock or calendar. Exercise 2: Order – Days of the Week and Months Drag the days of the week and the months of the year into correct order. Drag them to the correct drop zone boxes. Exercise 3: Time and Date, Questions and Answers Read the questions. Drag the best answer for each question to the drop zone box next to the question. Exercise 4: Time Vocabulary – Classification Put the draggable words next to the best classification. For example: 10:30 is a time, not a year. Drag the word “time” to the drop zone box next to 10:30.	pressbooks	2025-03-22T05:08:51.172383	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/8/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/9/	Grammar – Beginning Videos and Exercises Daily Routine Daily Routine – Watch the video and complete the exercises. Exercise 1: Daily Routine Phrases There are sixteen boxes below with pictures and words for daily routines. Click on the boxes and find the pictures that match the words. Exercise 2: Daily Routine Question and Answer Match the question with the correct answer. Drag the answer to the drop zone box next to each question. Exercise 3: Jennifer’s Daily Routine Read Jennifer’s daily activities and put the activities in time order. Drag the words to the drop zone boxes. Exercise 4: Daily Routine Extra Read the questions and answers. Drag the correct answer to the drop zone boxes next to each question.	pressbooks	2025-03-22T05:08:51.185757	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/9/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/10/	Grammar – Beginning Videos and Exercises Weather Weather – Watch the videos and complete the exercises. Exercise 1: Weather Statements Sixteen squares below show eight pictures of weather and eight words about weather. Click on the squares. Match the picture and the word. Exercise 2: Weather Dialog Read the conversation below. Drag the words to the correct drop zone boxes to complete the conversation. Exercise 3: Weather Questions and Answers Read the conversation below. Drag the words to the correct drop zone boxes to complete the conversation. Exercise 4: Weather Extra Read the conversation below. Drag the words to the correct drop zone box to complete the conversation.	pressbooks	2025-03-22T05:08:51.199861	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/10/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/1/	Grammar – High Beginning Videos and Exercises Greeting a Stranger Greeting a Stranger – Watch the videos and complete the exercises below. Exercise 1: Greeting a Stranger Drag the words and phrases to the drop zone boxes following the sentences. Click the “next” arrow. There are five questions in this series. Exercise 2: Greeting a Friend Drag the words to the drop zone boxes to complete the conversations. Click the “next” arrow. There are four questions in this series. Exercise 3: Greetings General Click on the eight cards below. Find the picture that matches the word. Exercise 4: Greetings Extra Type words in the boxes to complete the conversation.	pressbooks	2025-03-22T05:08:51.217063	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/1/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/3-2/	Grammar – High Beginning Videos and Exercises Ordinal Numbers Ordinal Numbers – Watch the videos and complete the exercises below. Exercise 1: Ordinal Numbers 1 Match the numbers and the words. Drag the words to the drop zone boxes next to the number. Exercise 2: Ordinal Numbers 2 Put ten written words ordinal numbers in order. Drag the words to the drop zone boxes. Exercise 3: Ordinal Numbers 3 Read the number. Type the words in the blank boxes.	pressbooks	2025-03-22T05:08:51.229676	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/3-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/2/	Grammar – High Beginning Videos and Exercises Vowels, Consonants, Nouns, and Articles Vowels, Consonants, Nouns, and Articles – Watch the videos and complete the exercises Exercise 1: Vowel or Consonant Mark the following alphabet letters, vowels or consonants. Click on one of the words in brackets following each letter. Exercise 2: A or An Type the correct article, “a” or “an,” in the blank box before each word. Exercise 3: Alphabetical Order Drag the twenty-six letters of the alphabet to the drop zone boxes. Put the letters in correct alphabetical order. Exercise 4: Alphabetical Order Extra There are twenty-six names listed. Put the names in alphabetical order. Drag the names to drop zone boxes.	pressbooks	2025-03-22T05:08:51.247125	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/61/	Grammar – High Beginning Videos and Exercises Possessive Adjectives and Pronouns Possessive Adjectives and Subjective Pronouns – Watch the videos and complete the exercises. Exercise 1: Possessive Pronouns Some of the sentences below have possessive pronouns missing. Drag the correct possessive pronouns to the drop zone boxes. Exercise 2: To-Be Verb Read the questions and responses. Match the correct response with the question. Drag the responses to the drop zone boxes following the questions. Exercise 3: Pronouns, Contractions, To-Be Verb There are two questions in this exercise. Exercise 1: Type contractions for each pronoun and form of “Be”. Click the check button. Click the next arrow to go to the second exercise. Exercise 2: Drag the correct work to the drop zone box. Exercise 4: To-Be Extra Click one one of the three words between brackets. Choose the correct form of “be” for each pronoun. Click the check button.	pressbooks	2025-03-22T05:08:51.259272	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/61/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/5-2/	Grammar – High Beginning Videos and Exercises Verb Be, Making Questions Verb Be and Making Questions – Watch the videos and complete the exercises. Exercise 1: Making Questions Turn the sentences into questions. Drag the words to the drop zone boxes to make a question. Click the check button. Click the next arrow to continue. There are seven questions in this exercise. Exercise 2: To-Be Verb Questions Choose the correct form of the “be” verb to make questions. Click on one of the three words between brackets. Complete all sentences and click the check button. Exercise 3: Affirmative and Negative Answers For each question make an affirmative and negative response. Drag words to the drop zone boxes. Click the next arrow to continue. There are six questions in this exercise. Exercise 4: To-Be Extra Drag words to the drop zone boxes to complete the sentences. Use the correct verb or pronoun to complete the sentence.	pressbooks	2025-03-22T05:08:51.271604	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/5-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/do-does-questions/	Grammar – High Beginning Videos and Exercises Questions with Do and Does Questions with Do/Does – Watch the videos and complete the exercises. Exercise 1: Have/Has Questions Read the questions. Drag answers to drop zone boxes following each question. Exercise 2: Have/Has Practice Read the story about a garden and a cat. Type the missing words in blank boxes in the story. Exercise 3: Adjectives Read the sentence. Click on the adjectives in the sentences. Click the check button. Exercise 4: To-Be, To-Have Review Read the sentences. Type the correct forms of “be” or “have to complete the sentences in the blank boxes in the story.	pressbooks	2025-03-22T05:08:51.283225	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/do-does-questions/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/have/	Grammar – High Beginning Videos and Exercises Questions with Be Verb and Have Questions using Be and Have – Watch the videos and complete the exercises. Exercise 1: Have and Has Type the words in blank boxes. Type the correct form of the verb “have” for each pronoun. Exercise 2: Negative Have and Has Read the sentences. Type the correct negative form of “have” in the blank boxes. Exercise 3: To Have Practice Read the sentences and choose the correct word. Click on one of the words between brackets. Complete all sentences and click the check button.	pressbooks	2025-03-22T05:08:51.294483	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/have/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/8-2/	Grammar – High Beginning Videos and Exercises This/That and Calendar Vocabulary This/That and Calendar Vocabulary – Watch the video and complete the exercises. Exercise 1: This and That Read the sentences. Choose the word “this” or “that” . Click on the word between the brackets. Click the check button. Exercise 2: These and Those Read the sentences. Choose the word “these” or “those”. Click on the word between the brackets. Click the check button. Exercise 3: Calendar Vocabulary This exercise has two questions. Question 1. Drag the word to the drop zone box to match the definition. Click the next arrow. Question 2. Type vocabulary words in the blanks. Exercise 4: Time Vocabulary Match the vocabulary. For example, 7:30 is “time”. Drag matching words to the drop zone boxes.	pressbooks	2025-03-22T05:08:51.306315	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/8-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/9-2/	Grammar – High Beginning Videos and Exercises Prepositions of Place and Time Prepositions of Place and Time – Watch the videos and complete the exercises. Exercise 1: In, On, Under This exercise has three questions. The first two exercises might be extra difficult with a screen reader. Question 1: Drag pictures to the correct drop zones in the picture. There is a bookcase and three cats. Read the instructions. Drag the cats to the correct drop zone on the bookcase picture. The bookcase has three shelves, a top, and space under the third shelf. The cats are different colors. Click check, then click the next arrow to go to the next question. Question 2: There are three drop zones: in, on, and under. Drag the pictures to the correct drop zone. The picture will not stay unless it is correct. Click check, then click the next arrow to go to the next question. Question 3: Read the sentences. Type the correct preposition in the blank box. Exercise 2: Times of Day Read the sentences. Type phrases for the time of day in the blank boxes. Exercise 3: Time and Date Questions and Answers Read the questions. Drag the best answers to the drop zone boxes next to the questions. Exercise 4: Daily Routine Questions and Answers Read the questions. Drag the best answers to the drop zone boxes next to the questions.	pressbooks	2025-03-22T05:08:51.319092	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/9-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/10-2/	Grammar – High Beginning Videos and Exercises Articles and Nouns Articles and Nouns – Watch the videos and complete the exercises. Exercise 1: Articles 1 Read the story. Click on the correct article found between the brackets. Use “0” for no article. Click the check button. Exercise 2: Articles 2 Read the story. Type the correct article in the blank boxes. When no article is needed, leave the blank box empty. Click the check button. Exercise 3: Singular and Plural Nouns Type the plural form of the word in the blank box next to the word. Spell the plurals correctly. Exercise 4: Weather Dialog Complete the conversations. Drag the best words to the drop zone boxes for each conversation.	pressbooks	2025-03-22T05:08:51.331188	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/10-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/1-2/	Grammar – Intermediate Videos and Exercises Pronouns, Adjectives, and Greetings Pronouns, Adjectives, and Greetings – Watch the videos and complete the exercises. Exercise 1: Pronouns 1 Read the story. Pronouns are missing in some of the sentences. Drag pronouns to the correct drop zone boxes. Exercise 2: Pronouns 2 Read the story. Words are missing from the sentences. Type the missing words in the blank boxes. Exercise 3: Adjectives Click on the adjectives in the following sentences. Click the check button. Exercise 4: Greetings Read the conversation. Type words in the blank boxes below to complete the conversation.	pressbooks	2025-03-22T05:08:51.343089	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/1-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/2-2/	Grammar – Intermediate Videos and Exercises Present Tenses and Parts of Speech Present Tenses and Parts of Speech – Watch the videos and complete the exercises. Exercise 1: Simple Present Continuous There are eight questions in this exercise. Follow instructions for each question. Click the “check” button. Click the “arrow” button to go to the next question. Exercise 2: Adverb Intensifiers Type the words in the blank boxes below. Exercise 3: Present Simple Type correct form of the verb in the blank boxes of each sentence. Use the verb in parenthesis at the end of each sentence. Click the “check” button. Exercise 4: To Be – Verb Click the correct form of the verb “be” between the brackets. Click the “check” button.	pressbooks	2025-03-22T05:08:51.355312	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/2-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/2-3/	Grammar – Intermediate Videos and Exercises Simple Past and Future with Be Verb Past Simple, Future, To Be – Watch the videos and complete the exercises. Exercise 1: Past Simple There are four questions in this exercise. Follow the instructions for each question. Click the “check” button and “next” arrow. Exercise 2: Future – Be Going To There are eight drag and drop questions in this exercise. For each question drag the words to the correct drop zone box to make a sentence or question. Click the “check” button and “next” arrow. Exercise 3: Future – Present Continuous Type the correct form and tense of the verbs in the blank boxes. Click on the “check” button. Exercise 4: To – Be Verb The sentences are missing words. Drag the words to the correct drop zone boxes to complete the sentences.	pressbooks	2025-03-22T05:08:51.368040	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/2-3/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/4-2/	Grammar – Intermediate Videos and Exercises Negative Sentences and Questions Negative Sentences and Questions – Watch the videos and complete the exercises. Exercise 1: Negative Sentences There are three questions in this exercise. Follow the instructions for each question. Click the “check” button and “next” arrow. Exercise 2: Making Questions This exercise has seven questions. Drag the words to the correct drop zone boxes. Use the words to form a question. Click the “check” button. Click the “next” arrow. Exercise 3: To Have Click on the words between the brackets. Choose the best word to complete the sentences. Click on the “check” button. Exercise 4: Have Positive and Negative Type the correct form of “to have” in the blank boxes below. Notice the negatives.	pressbooks	2025-03-22T05:08:51.380719	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/4-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/verb-patterns/	Grammar – Intermediate Videos and Exercises Verb Patterns Verb Patterns – Watch the videos and complete the exercises. Exercise 1: There are three questions in this exercise. Drag the words to the correct drop zone boxes. Click the “check” button. Click the “next” arrow.	pressbooks	2025-03-22T05:08:51.391757	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/verb-patterns/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/articles-indefinite-and-definite/	Grammar – Intermediate Videos and Exercises Articles – Indefinite and Definite Articles – Indefinite and Definite – Watch the videos and complete the exercises. Exercise 1: Articles 1 Read the sentences. Click on the correct article between brackets. Click on “0” for no article. Exercise 2: Articles 2 Type the correct article for each noun in the story in the blank box. Use articles a, an, the. Leave the blank box empty if no article is needed. Click the “check” button. Exercise 3: In, On, Under This exercise has three questions. The first two exercises might be extra difficult with a screen reader. Question 1: Drag pictures to the correct drop zones in the picture. There is a bookcase and three cats. Read the instructions. Drag the cats to the correct drop zone on the bookcase picture. The bookcase has three shelves, a top, and space under the third shelf. The cats are different colors. Click the check button, then click the next arrow to go to the next question. Question 2: There are three drop zones: in, on, and under. Drag the pictures to the correct drop zone. The picture will not stay unless it is correct. Click the check button, then click the next arrow to go to the next question. Question 3: Read the sentences. Type the correct preposition in the blank box.	pressbooks	2025-03-22T05:08:51.406585	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/articles-indefinite-and-definite/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/quantity/	Grammar – Intermediate Videos and Exercises Quantity Quantity – Watch the videos and complete the exercises. Exercise 1: Quantity Expressions 1 Click on the words in the brackets. For each word decide if it is “count” or “non-count” and click on the quantity word, “much” or “many”. Click the “check” button. Exercise 2: Quantity Expressions 2 Read the story. Type “a little” or ” a few” in the blank boxes. Click the “check” button. Exercise 3: Adjectives Click on the adjectives in the sentences. Click the “check” button. Exercise 4: Singular and Plural Nouns Type the plural of each noun in the blank boxes below.	pressbooks	2025-03-22T05:08:51.419946	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/quantity/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/some-and-any/	Grammar – Intermediate Videos and Exercises Some and Any Some and Any – Watch the videos and complete the exercises. Exercises 1: Some and Any Read the conversation. Type the words “some” or “any” in the blank boxes below. Click the “check” button. Exercises 2: Something, Anything, Nothing Read the conversation. Complete the sentence with draggable words provided. Drag the words to the drop box zones in each sentence. Exercises 3: This and That Click on the words “this” or “that” between brackets to complete the sentences. Click the “check” button. Exercises 4: These and Those Click on the words “these” or “those” between brackets to complete the sentences. Click the “check” button.	pressbooks	2025-03-22T05:08:51.432131	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/some-and-any/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/preposition-time/	Grammar – Intermediate Videos and Exercises Prepositions of Time Prepositions – In, On, At, with Time – Watch the video and complete the exercises. Exercise 1: Prepositions of Time There are three questions in this exercise. Follow instructions for each question. Click the “check” button. Click the “arrow” button to go to the next question. Exercise 2: Time Vocabulary Drag the words to the drop zone boxes to categorize the words. For example: the word “January” is a month. Drag the word month to the drop zone box next to January. Exercise 3: Calendar Vocabulary There are two questions in this exercise. Question 1: Drag the words to drop zone boxes next to each period of time. Question 2: Type the word Click the “next” arrow. Exercise 4: Date and Time Read the questions about time. Drag the answers to the drop zone box next to the question.	pressbooks	2025-03-22T05:08:51.444866	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/preposition-time/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/comparative-adjectives/	Grammar – Intermediate Videos and Exercises Comparative Adjectives Comparative Adjectives -Watch the videos and complete the exercises. Exercise 1: Comparatives 1 Read the sentences. Type the correct comparative adjective in the blank box of the second sentence. Click the check button. Exercise 2: Comparatives 2 Type the adjective or comparative adjective in the blanks. Click the check button. Exercise 3: Weather Dialog Complete the conversations. Drag words to the drop zone boxes.	pressbooks	2025-03-22T05:08:51.456220	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/comparative-adjectives/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.476743	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-1	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.496973	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-1", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-2	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.520715	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-2", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-3	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.540646	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-3", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-4	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.561087	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-4", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-5	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.580819	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-5", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-6	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.605295	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-6", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-7	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.625817	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-7", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-8	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.646799	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-8", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-9	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.668600	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-9", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-10	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.688175	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-10", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/	Conversational AI Theory 2 Natural Language Processing Developing a computer application that can accurately parse and interpret how humans communicate has been a decades long struggle. Natural language is defined as a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language. The English language has around 600,000 words, within these words are complex grammatical constructs, different meanings and interpretations. This makes trying to develop a rule-based system that is capable of interpreting and speaking natural language next to impossible. History of Natural Language Processing Natural language processing is the technique that is used to decode, interpret and understand natural language. There have been essentially three generations of Virtual Language processing strategies that conversation all systems. These generations are Symbolic, Statistical and Neural. Symbolic Natural Language Processing was first introduced in the 1950s. Symbolic NLP functions by having a list of rules that the computer can use to evaluate and process language data. Symbolic systems were often used to solve language translation, as well as preform entity substitution into user utterances. For example a user might say “My hand hurts.” The chat bot could then reply with something like “Why does your hand hurt?” Unfortunately these types of systems lacked any contextual awareness. Statistical NLP By the 1990s the majority of conversational systems had moved to using statistical analysis of natural language. This type of processing required increased computing resources. The shift from Symbolic NLP to statistical represented a shift from fixed rules to probabilistic AI processes. These solutions relied on what is known as text corpora which is a sample of data (actual language) that contains meaningful context. This data can then be used to preform statistical analysis, hypothesis testing or validating linguistic rules. A corpus is a representative sample of actual language production within a meaningful context and with a general purpose. This can be thought of as training data for machine learning system. Source: https://odsc.medium.com/20-open-datasets-for-natural-language-processing-538fbfaf8e38 General \| \| \| Enron Dataset: \| Over half a million anonymized emails from over 100 users. It’s one of the few publicly available collections of “real” emails available for study and training sets. \| \| e Blogger Corpus: \| Nearly 700,000 blog posts from blogger.com. The meat of the blogs contain commonly occurring English words, at least 200 of them in each entry. \| \| SMS Spam Collection : \| Excellent dataset focused on spam. Nearly 6000 messages tagged as legitimate or spam messages with a useful subset extracted directly from Grumbletext. \| \| Recommender Systems Datasets : \| Datasets from a variety of sources, including fitness tracking, video games, song data, and social media. Labels include star ratings, time stamps, social networks, and images. \| \| Project Gutenberg : \| Extensive collection of book texts. These are public domain and available in a variety of languages, spanning a long period of time.\ \| \| Sentiment 140 : \| 160,000 tweets scrubbed of emoticons. They’re arranged in six fields — polarity, tweet date, user, text, query, and ID. \| \| MultiDomain Sentiment Analysis Dataset : \| Includes a wide range of Amazon reviews. Dataset can be converted to binary labels based on star review, and some product categories have thousands of entries. \| \| Yelp Reviews : \| Restaurant rankings and reviews. It includes a variety of aspects including reviews for sentiment analysis plus a challenge with cash prizes for those working with Yelp’s datasets. \| \| Dictionaries for Movies and Finance : \| Specific dictionaries for sentiment analysis using a specific field for testing data. Entries are clean and arranged in positive or negative connotations. \| \| OpinRank Dataset : \| 300,000 reviews from Edmunds and TripAdvisor. They’re neatly arranged by car model or by travel destination and relevant to the hotel. \| Text \| \| \| 20 Newsgroups : \| 20,000 documents from over 20 different newsgroups. The content covers a variety of topics with some closely related for reference. There are three versions, one in its original form, one with dates removed, and one with duplicates removed. \| \| The WikiQA Corpus : \| Contains question and sentence pairs. It’s robust and compiled from Bing query logs. There are over 3000 questions and over 29,000 answer sentences with just under 1500 labeled as answer sentences. \| \| European Parliament Proceedings Parallel Corpus : \| Sentence pairs from Parliament proceedings. There are entries from 21 European languages including some less common entries for ML corpus. \| \| Jeopardy : \| Over 200,000 questions from the famed tv show. It includes category and value designations as well as other descriptors like question and answer fields and rounds. \| \| Legal Case Reports Dataset : \| Text summaries of legal cases. It contains wrapups of over 4000 legal cases and could be great for training for automatic text summarization. \| Speech \| \| \| LibriSpeech : \| Nearly 1000 hours of speech in English taken from audiobook clips. \| \| Spoken Wikipedia Corpora : \| Spoken articles from Wikipedia in three languages, English, German, and Dutch. It includes a diverse speaker set and range of topics. There are hundreds of hours available for training sets. \| \| LJ Speech Dataset : \| 13,100 clips of short passages from audiobooks. They vary in length but contain a single speaker and include a transcription of the audio, which has been verified by a human reader. \| \| M-AI Labs Speech Dataset : \| Nearly 1000 hours of audio plus transcriptions. It includes multiple languages arranged by male voices, female voices, and a mix of the two. \| Neural Natural Language processing utilize deep neural network machine learning that results in enhanced language modeling and parsing. The majority of natural language processing solutions developed in the last 10 years generally use Neural NLP. The use of Neural networks greatly improves the ability of the capability of a NLP system to model, learn and reason. While also greatly reducing the amount of human perpetration of these systems. Key Natural Language concepts NLP Modeling – The modeling of natural language can refer to a number of different aspects such as encoding and decoding a sentence, creating sequence of labels. Using modern neural network in NLP this type of modeling allows for the breakdown of language into millions of trainable nodes that can preform syntactical semantic and sentiment analysis, language translation, topic extraction, classification and next sentence prediction. NLP Reasoning – Also known as common-sense reasoning is ability to allow computer to better interact and understand human interaction by collecting assumptions and extracting meaning behind the text provided but refining the assumptions throughout the duration of the conversation. In many ways common sense is the application of pragmatics in a conversational. Being situational aware of the context of the conversation is extremely important to interpreting meaning. This area of NLP, focusing on trying to reduce the “absurd” mistakes that NLP systems can make when they “jump to the wrong conclusion” due to a lack of commonsense. Natural Language Generation – This is the process by which the the machine learning algorithm creates natural language in such a way that is indistinguishable from a human response. This relies on constructing proper sentences that are suitable for the target demographics of users, without sounding to rigid or robotic. The key is to properly format and present the required response back to the user there are many ways to achive this one of the most common was is using statistical responses based from a large corpus of human text. Overview of a modern Conversational AI system. Natural Language Processing is an important part of a conversational AI system. It is this processing that is used to review the user’s input and prepare a reply back. Often conversational systems use voice interactions. In these cases the NLP is surrounded by a speech-to-text and text-to-speech process that is used to interact directly using voice. These voice replies utilize a synthetic voice engine which is modeled after the human voice. The goal of the synthesized voice is to provide a clear and easy to understand voice along while also attempting to mimic human speech inflections. \| Speech to Text Used to decode sound waves translate this into a textual representation of exactly what has been said. \| Natural Language Processing Evaluates the provided input, determines the correct response and formats the reply into natural language. \| Text to Speech The computer will formulate a response and translate a written text string into and auditory signal.. \| Natural Language is a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language.	pressbooks	2025-03-22T05:08:51.709520	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/#chapter-22-section-1	Conversational AI Theory 2 Natural Language Processing Developing a computer application that can accurately parse and interpret how humans communicate has been a decades long struggle. Natural language is defined as a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language. The English language has around 600,000 words, within these words are complex grammatical constructs, different meanings and interpretations. This makes trying to develop a rule-based system that is capable of interpreting and speaking natural language next to impossible. History of Natural Language Processing Natural language processing is the technique that is used to decode, interpret and understand natural language. There have been essentially three generations of Virtual Language processing strategies that conversation all systems. These generations are Symbolic, Statistical and Neural. Symbolic Natural Language Processing was first introduced in the 1950s. Symbolic NLP functions by having a list of rules that the computer can use to evaluate and process language data. Symbolic systems were often used to solve language translation, as well as preform entity substitution into user utterances. For example a user might say “My hand hurts.” The chat bot could then reply with something like “Why does your hand hurt?” Unfortunately these types of systems lacked any contextual awareness. Statistical NLP By the 1990s the majority of conversational systems had moved to using statistical analysis of natural language. This type of processing required increased computing resources. The shift from Symbolic NLP to statistical represented a shift from fixed rules to probabilistic AI processes. These solutions relied on what is known as text corpora which is a sample of data (actual language) that contains meaningful context. This data can then be used to preform statistical analysis, hypothesis testing or validating linguistic rules. A corpus is a representative sample of actual language production within a meaningful context and with a general purpose. This can be thought of as training data for machine learning system. Source: https://odsc.medium.com/20-open-datasets-for-natural-language-processing-538fbfaf8e38 General \| \| \| Enron Dataset: \| Over half a million anonymized emails from over 100 users. It’s one of the few publicly available collections of “real” emails available for study and training sets. \| \| e Blogger Corpus: \| Nearly 700,000 blog posts from blogger.com. The meat of the blogs contain commonly occurring English words, at least 200 of them in each entry. \| \| SMS Spam Collection : \| Excellent dataset focused on spam. Nearly 6000 messages tagged as legitimate or spam messages with a useful subset extracted directly from Grumbletext. \| \| Recommender Systems Datasets : \| Datasets from a variety of sources, including fitness tracking, video games, song data, and social media. Labels include star ratings, time stamps, social networks, and images. \| \| Project Gutenberg : \| Extensive collection of book texts. These are public domain and available in a variety of languages, spanning a long period of time.\ \| \| Sentiment 140 : \| 160,000 tweets scrubbed of emoticons. They’re arranged in six fields — polarity, tweet date, user, text, query, and ID. \| \| MultiDomain Sentiment Analysis Dataset : \| Includes a wide range of Amazon reviews. Dataset can be converted to binary labels based on star review, and some product categories have thousands of entries. \| \| Yelp Reviews : \| Restaurant rankings and reviews. It includes a variety of aspects including reviews for sentiment analysis plus a challenge with cash prizes for those working with Yelp’s datasets. \| \| Dictionaries for Movies and Finance : \| Specific dictionaries for sentiment analysis using a specific field for testing data. Entries are clean and arranged in positive or negative connotations. \| \| OpinRank Dataset : \| 300,000 reviews from Edmunds and TripAdvisor. They’re neatly arranged by car model or by travel destination and relevant to the hotel. \| Text \| \| \| 20 Newsgroups : \| 20,000 documents from over 20 different newsgroups. The content covers a variety of topics with some closely related for reference. There are three versions, one in its original form, one with dates removed, and one with duplicates removed. \| \| The WikiQA Corpus : \| Contains question and sentence pairs. It’s robust and compiled from Bing query logs. There are over 3000 questions and over 29,000 answer sentences with just under 1500 labeled as answer sentences. \| \| European Parliament Proceedings Parallel Corpus : \| Sentence pairs from Parliament proceedings. There are entries from 21 European languages including some less common entries for ML corpus. \| \| Jeopardy : \| Over 200,000 questions from the famed tv show. It includes category and value designations as well as other descriptors like question and answer fields and rounds. \| \| Legal Case Reports Dataset : \| Text summaries of legal cases. It contains wrapups of over 4000 legal cases and could be great for training for automatic text summarization. \| Speech \| \| \| LibriSpeech : \| Nearly 1000 hours of speech in English taken from audiobook clips. \| \| Spoken Wikipedia Corpora : \| Spoken articles from Wikipedia in three languages, English, German, and Dutch. It includes a diverse speaker set and range of topics. There are hundreds of hours available for training sets. \| \| LJ Speech Dataset : \| 13,100 clips of short passages from audiobooks. They vary in length but contain a single speaker and include a transcription of the audio, which has been verified by a human reader. \| \| M-AI Labs Speech Dataset : \| Nearly 1000 hours of audio plus transcriptions. It includes multiple languages arranged by male voices, female voices, and a mix of the two. \| Neural Natural Language processing utilize deep neural network machine learning that results in enhanced language modeling and parsing. The majority of natural language processing solutions developed in the last 10 years generally use Neural NLP. The use of Neural networks greatly improves the ability of the capability of a NLP system to model, learn and reason. While also greatly reducing the amount of human perpetration of these systems. Key Natural Language concepts NLP Modeling – The modeling of natural language can refer to a number of different aspects such as encoding and decoding a sentence, creating sequence of labels. Using modern neural network in NLP this type of modeling allows for the breakdown of language into millions of trainable nodes that can preform syntactical semantic and sentiment analysis, language translation, topic extraction, classification and next sentence prediction. NLP Reasoning – Also known as common-sense reasoning is ability to allow computer to better interact and understand human interaction by collecting assumptions and extracting meaning behind the text provided but refining the assumptions throughout the duration of the conversation. In many ways common sense is the application of pragmatics in a conversational. Being situational aware of the context of the conversation is extremely important to interpreting meaning. This area of NLP, focusing on trying to reduce the “absurd” mistakes that NLP systems can make when they “jump to the wrong conclusion” due to a lack of commonsense. Natural Language Generation – This is the process by which the the machine learning algorithm creates natural language in such a way that is indistinguishable from a human response. This relies on constructing proper sentences that are suitable for the target demographics of users, without sounding to rigid or robotic. The key is to properly format and present the required response back to the user there are many ways to achive this one of the most common was is using statistical responses based from a large corpus of human text. Overview of a modern Conversational AI system. Natural Language Processing is an important part of a conversational AI system. It is this processing that is used to review the user’s input and prepare a reply back. Often conversational systems use voice interactions. In these cases the NLP is surrounded by a speech-to-text and text-to-speech process that is used to interact directly using voice. These voice replies utilize a synthetic voice engine which is modeled after the human voice. The goal of the synthesized voice is to provide a clear and easy to understand voice along while also attempting to mimic human speech inflections. \| Speech to Text Used to decode sound waves translate this into a textual representation of exactly what has been said. \| Natural Language Processing Evaluates the provided input, determines the correct response and formats the reply into natural language. \| Text to Speech The computer will formulate a response and translate a written text string into and auditory signal.. \| Natural Language is a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language.	pressbooks	2025-03-22T05:08:51.730449	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/#chapter-22-section-1", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/#chapter-22-section-2	Conversational AI Theory 2 Natural Language Processing Developing a computer application that can accurately parse and interpret how humans communicate has been a decades long struggle. Natural language is defined as a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language. The English language has around 600,000 words, within these words are complex grammatical constructs, different meanings and interpretations. This makes trying to develop a rule-based system that is capable of interpreting and speaking natural language next to impossible. History of Natural Language Processing Natural language processing is the technique that is used to decode, interpret and understand natural language. There have been essentially three generations of Virtual Language processing strategies that conversation all systems. These generations are Symbolic, Statistical and Neural. Symbolic Natural Language Processing was first introduced in the 1950s. Symbolic NLP functions by having a list of rules that the computer can use to evaluate and process language data. Symbolic systems were often used to solve language translation, as well as preform entity substitution into user utterances. For example a user might say “My hand hurts.” The chat bot could then reply with something like “Why does your hand hurt?” Unfortunately these types of systems lacked any contextual awareness. Statistical NLP By the 1990s the majority of conversational systems had moved to using statistical analysis of natural language. This type of processing required increased computing resources. The shift from Symbolic NLP to statistical represented a shift from fixed rules to probabilistic AI processes. These solutions relied on what is known as text corpora which is a sample of data (actual language) that contains meaningful context. This data can then be used to preform statistical analysis, hypothesis testing or validating linguistic rules. A corpus is a representative sample of actual language production within a meaningful context and with a general purpose. This can be thought of as training data for machine learning system. Source: https://odsc.medium.com/20-open-datasets-for-natural-language-processing-538fbfaf8e38 General \| \| \| Enron Dataset: \| Over half a million anonymized emails from over 100 users. It’s one of the few publicly available collections of “real” emails available for study and training sets. \| \| e Blogger Corpus: \| Nearly 700,000 blog posts from blogger.com. The meat of the blogs contain commonly occurring English words, at least 200 of them in each entry. \| \| SMS Spam Collection : \| Excellent dataset focused on spam. Nearly 6000 messages tagged as legitimate or spam messages with a useful subset extracted directly from Grumbletext. \| \| Recommender Systems Datasets : \| Datasets from a variety of sources, including fitness tracking, video games, song data, and social media. Labels include star ratings, time stamps, social networks, and images. \| \| Project Gutenberg : \| Extensive collection of book texts. These are public domain and available in a variety of languages, spanning a long period of time.\ \| \| Sentiment 140 : \| 160,000 tweets scrubbed of emoticons. They’re arranged in six fields — polarity, tweet date, user, text, query, and ID. \| \| MultiDomain Sentiment Analysis Dataset : \| Includes a wide range of Amazon reviews. Dataset can be converted to binary labels based on star review, and some product categories have thousands of entries. \| \| Yelp Reviews : \| Restaurant rankings and reviews. It includes a variety of aspects including reviews for sentiment analysis plus a challenge with cash prizes for those working with Yelp’s datasets. \| \| Dictionaries for Movies and Finance : \| Specific dictionaries for sentiment analysis using a specific field for testing data. Entries are clean and arranged in positive or negative connotations. \| \| OpinRank Dataset : \| 300,000 reviews from Edmunds and TripAdvisor. They’re neatly arranged by car model or by travel destination and relevant to the hotel. \| Text \| \| \| 20 Newsgroups : \| 20,000 documents from over 20 different newsgroups. The content covers a variety of topics with some closely related for reference. There are three versions, one in its original form, one with dates removed, and one with duplicates removed. \| \| The WikiQA Corpus : \| Contains question and sentence pairs. It’s robust and compiled from Bing query logs. There are over 3000 questions and over 29,000 answer sentences with just under 1500 labeled as answer sentences. \| \| European Parliament Proceedings Parallel Corpus : \| Sentence pairs from Parliament proceedings. There are entries from 21 European languages including some less common entries for ML corpus. \| \| Jeopardy : \| Over 200,000 questions from the famed tv show. It includes category and value designations as well as other descriptors like question and answer fields and rounds. \| \| Legal Case Reports Dataset : \| Text summaries of legal cases. It contains wrapups of over 4000 legal cases and could be great for training for automatic text summarization. \| Speech \| \| \| LibriSpeech : \| Nearly 1000 hours of speech in English taken from audiobook clips. \| \| Spoken Wikipedia Corpora : \| Spoken articles from Wikipedia in three languages, English, German, and Dutch. It includes a diverse speaker set and range of topics. There are hundreds of hours available for training sets. \| \| LJ Speech Dataset : \| 13,100 clips of short passages from audiobooks. They vary in length but contain a single speaker and include a transcription of the audio, which has been verified by a human reader. \| \| M-AI Labs Speech Dataset : \| Nearly 1000 hours of audio plus transcriptions. It includes multiple languages arranged by male voices, female voices, and a mix of the two. \| Neural Natural Language processing utilize deep neural network machine learning that results in enhanced language modeling and parsing. The majority of natural language processing solutions developed in the last 10 years generally use Neural NLP. The use of Neural networks greatly improves the ability of the capability of a NLP system to model, learn and reason. While also greatly reducing the amount of human perpetration of these systems. Key Natural Language concepts NLP Modeling – The modeling of natural language can refer to a number of different aspects such as encoding and decoding a sentence, creating sequence of labels. Using modern neural network in NLP this type of modeling allows for the breakdown of language into millions of trainable nodes that can preform syntactical semantic and sentiment analysis, language translation, topic extraction, classification and next sentence prediction. NLP Reasoning – Also known as common-sense reasoning is ability to allow computer to better interact and understand human interaction by collecting assumptions and extracting meaning behind the text provided but refining the assumptions throughout the duration of the conversation. In many ways common sense is the application of pragmatics in a conversational. Being situational aware of the context of the conversation is extremely important to interpreting meaning. This area of NLP, focusing on trying to reduce the “absurd” mistakes that NLP systems can make when they “jump to the wrong conclusion” due to a lack of commonsense. Natural Language Generation – This is the process by which the the machine learning algorithm creates natural language in such a way that is indistinguishable from a human response. This relies on constructing proper sentences that are suitable for the target demographics of users, without sounding to rigid or robotic. The key is to properly format and present the required response back to the user there are many ways to achive this one of the most common was is using statistical responses based from a large corpus of human text. Overview of a modern Conversational AI system. Natural Language Processing is an important part of a conversational AI system. It is this processing that is used to review the user’s input and prepare a reply back. Often conversational systems use voice interactions. In these cases the NLP is surrounded by a speech-to-text and text-to-speech process that is used to interact directly using voice. These voice replies utilize a synthetic voice engine which is modeled after the human voice. The goal of the synthesized voice is to provide a clear and easy to understand voice along while also attempting to mimic human speech inflections. \| Speech to Text Used to decode sound waves translate this into a textual representation of exactly what has been said. \| Natural Language Processing Evaluates the provided input, determines the correct response and formats the reply into natural language. \| Text to Speech The computer will formulate a response and translate a written text string into and auditory signal.. \| Natural Language is a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language.	pressbooks	2025-03-22T05:08:51.748705	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/#chapter-22-section-2", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/#chapter-22-section-3	Conversational AI Theory 2 Natural Language Processing Developing a computer application that can accurately parse and interpret how humans communicate has been a decades long struggle. Natural language is defined as a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language. The English language has around 600,000 words, within these words are complex grammatical constructs, different meanings and interpretations. This makes trying to develop a rule-based system that is capable of interpreting and speaking natural language next to impossible. History of Natural Language Processing Natural language processing is the technique that is used to decode, interpret and understand natural language. There have been essentially three generations of Virtual Language processing strategies that conversation all systems. These generations are Symbolic, Statistical and Neural. Symbolic Natural Language Processing was first introduced in the 1950s. Symbolic NLP functions by having a list of rules that the computer can use to evaluate and process language data. Symbolic systems were often used to solve language translation, as well as preform entity substitution into user utterances. For example a user might say “My hand hurts.” The chat bot could then reply with something like “Why does your hand hurt?” Unfortunately these types of systems lacked any contextual awareness. Statistical NLP By the 1990s the majority of conversational systems had moved to using statistical analysis of natural language. This type of processing required increased computing resources. The shift from Symbolic NLP to statistical represented a shift from fixed rules to probabilistic AI processes. These solutions relied on what is known as text corpora which is a sample of data (actual language) that contains meaningful context. This data can then be used to preform statistical analysis, hypothesis testing or validating linguistic rules. A corpus is a representative sample of actual language production within a meaningful context and with a general purpose. This can be thought of as training data for machine learning system. Source: https://odsc.medium.com/20-open-datasets-for-natural-language-processing-538fbfaf8e38 General \| \| \| Enron Dataset: \| Over half a million anonymized emails from over 100 users. It’s one of the few publicly available collections of “real” emails available for study and training sets. \| \| e Blogger Corpus: \| Nearly 700,000 blog posts from blogger.com. The meat of the blogs contain commonly occurring English words, at least 200 of them in each entry. \| \| SMS Spam Collection : \| Excellent dataset focused on spam. Nearly 6000 messages tagged as legitimate or spam messages with a useful subset extracted directly from Grumbletext. \| \| Recommender Systems Datasets : \| Datasets from a variety of sources, including fitness tracking, video games, song data, and social media. Labels include star ratings, time stamps, social networks, and images. \| \| Project Gutenberg : \| Extensive collection of book texts. These are public domain and available in a variety of languages, spanning a long period of time.\ \| \| Sentiment 140 : \| 160,000 tweets scrubbed of emoticons. They’re arranged in six fields — polarity, tweet date, user, text, query, and ID. \| \| MultiDomain Sentiment Analysis Dataset : \| Includes a wide range of Amazon reviews. Dataset can be converted to binary labels based on star review, and some product categories have thousands of entries. \| \| Yelp Reviews : \| Restaurant rankings and reviews. It includes a variety of aspects including reviews for sentiment analysis plus a challenge with cash prizes for those working with Yelp’s datasets. \| \| Dictionaries for Movies and Finance : \| Specific dictionaries for sentiment analysis using a specific field for testing data. Entries are clean and arranged in positive or negative connotations. \| \| OpinRank Dataset : \| 300,000 reviews from Edmunds and TripAdvisor. They’re neatly arranged by car model or by travel destination and relevant to the hotel. \| Text \| \| \| 20 Newsgroups : \| 20,000 documents from over 20 different newsgroups. The content covers a variety of topics with some closely related for reference. There are three versions, one in its original form, one with dates removed, and one with duplicates removed. \| \| The WikiQA Corpus : \| Contains question and sentence pairs. It’s robust and compiled from Bing query logs. There are over 3000 questions and over 29,000 answer sentences with just under 1500 labeled as answer sentences. \| \| European Parliament Proceedings Parallel Corpus : \| Sentence pairs from Parliament proceedings. There are entries from 21 European languages including some less common entries for ML corpus. \| \| Jeopardy : \| Over 200,000 questions from the famed tv show. It includes category and value designations as well as other descriptors like question and answer fields and rounds. \| \| Legal Case Reports Dataset : \| Text summaries of legal cases. It contains wrapups of over 4000 legal cases and could be great for training for automatic text summarization. \| Speech \| \| \| LibriSpeech : \| Nearly 1000 hours of speech in English taken from audiobook clips. \| \| Spoken Wikipedia Corpora : \| Spoken articles from Wikipedia in three languages, English, German, and Dutch. It includes a diverse speaker set and range of topics. There are hundreds of hours available for training sets. \| \| LJ Speech Dataset : \| 13,100 clips of short passages from audiobooks. They vary in length but contain a single speaker and include a transcription of the audio, which has been verified by a human reader. \| \| M-AI Labs Speech Dataset : \| Nearly 1000 hours of audio plus transcriptions. It includes multiple languages arranged by male voices, female voices, and a mix of the two. \| Neural Natural Language processing utilize deep neural network machine learning that results in enhanced language modeling and parsing. The majority of natural language processing solutions developed in the last 10 years generally use Neural NLP. The use of Neural networks greatly improves the ability of the capability of a NLP system to model, learn and reason. While also greatly reducing the amount of human perpetration of these systems. Key Natural Language concepts NLP Modeling – The modeling of natural language can refer to a number of different aspects such as encoding and decoding a sentence, creating sequence of labels. Using modern neural network in NLP this type of modeling allows for the breakdown of language into millions of trainable nodes that can preform syntactical semantic and sentiment analysis, language translation, topic extraction, classification and next sentence prediction. NLP Reasoning – Also known as common-sense reasoning is ability to allow computer to better interact and understand human interaction by collecting assumptions and extracting meaning behind the text provided but refining the assumptions throughout the duration of the conversation. In many ways common sense is the application of pragmatics in a conversational. Being situational aware of the context of the conversation is extremely important to interpreting meaning. This area of NLP, focusing on trying to reduce the “absurd” mistakes that NLP systems can make when they “jump to the wrong conclusion” due to a lack of commonsense. Natural Language Generation – This is the process by which the the machine learning algorithm creates natural language in such a way that is indistinguishable from a human response. This relies on constructing proper sentences that are suitable for the target demographics of users, without sounding to rigid or robotic. The key is to properly format and present the required response back to the user there are many ways to achive this one of the most common was is using statistical responses based from a large corpus of human text. Overview of a modern Conversational AI system. Natural Language Processing is an important part of a conversational AI system. It is this processing that is used to review the user’s input and prepare a reply back. Often conversational systems use voice interactions. In these cases the NLP is surrounded by a speech-to-text and text-to-speech process that is used to interact directly using voice. These voice replies utilize a synthetic voice engine which is modeled after the human voice. The goal of the synthesized voice is to provide a clear and easy to understand voice along while also attempting to mimic human speech inflections. \| Speech to Text Used to decode sound waves translate this into a textual representation of exactly what has been said. \| Natural Language Processing Evaluates the provided input, determines the correct response and formats the reply into natural language. \| Text to Speech The computer will formulate a response and translate a written text string into and auditory signal.. \| Natural Language is a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language.	pressbooks	2025-03-22T05:08:51.767178	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/#chapter-22-section-3", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/#chapter-22-section-4	Conversational AI Theory 2 Natural Language Processing Developing a computer application that can accurately parse and interpret how humans communicate has been a decades long struggle. Natural language is defined as a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language. The English language has around 600,000 words, within these words are complex grammatical constructs, different meanings and interpretations. This makes trying to develop a rule-based system that is capable of interpreting and speaking natural language next to impossible. History of Natural Language Processing Natural language processing is the technique that is used to decode, interpret and understand natural language. There have been essentially three generations of Virtual Language processing strategies that conversation all systems. These generations are Symbolic, Statistical and Neural. Symbolic Natural Language Processing was first introduced in the 1950s. Symbolic NLP functions by having a list of rules that the computer can use to evaluate and process language data. Symbolic systems were often used to solve language translation, as well as preform entity substitution into user utterances. For example a user might say “My hand hurts.” The chat bot could then reply with something like “Why does your hand hurt?” Unfortunately these types of systems lacked any contextual awareness. Statistical NLP By the 1990s the majority of conversational systems had moved to using statistical analysis of natural language. This type of processing required increased computing resources. The shift from Symbolic NLP to statistical represented a shift from fixed rules to probabilistic AI processes. These solutions relied on what is known as text corpora which is a sample of data (actual language) that contains meaningful context. This data can then be used to preform statistical analysis, hypothesis testing or validating linguistic rules. A corpus is a representative sample of actual language production within a meaningful context and with a general purpose. This can be thought of as training data for machine learning system. Source: https://odsc.medium.com/20-open-datasets-for-natural-language-processing-538fbfaf8e38 General \| \| \| Enron Dataset: \| Over half a million anonymized emails from over 100 users. It’s one of the few publicly available collections of “real” emails available for study and training sets. \| \| e Blogger Corpus: \| Nearly 700,000 blog posts from blogger.com. The meat of the blogs contain commonly occurring English words, at least 200 of them in each entry. \| \| SMS Spam Collection : \| Excellent dataset focused on spam. Nearly 6000 messages tagged as legitimate or spam messages with a useful subset extracted directly from Grumbletext. \| \| Recommender Systems Datasets : \| Datasets from a variety of sources, including fitness tracking, video games, song data, and social media. Labels include star ratings, time stamps, social networks, and images. \| \| Project Gutenberg : \| Extensive collection of book texts. These are public domain and available in a variety of languages, spanning a long period of time.\ \| \| Sentiment 140 : \| 160,000 tweets scrubbed of emoticons. They’re arranged in six fields — polarity, tweet date, user, text, query, and ID. \| \| MultiDomain Sentiment Analysis Dataset : \| Includes a wide range of Amazon reviews. Dataset can be converted to binary labels based on star review, and some product categories have thousands of entries. \| \| Yelp Reviews : \| Restaurant rankings and reviews. It includes a variety of aspects including reviews for sentiment analysis plus a challenge with cash prizes for those working with Yelp’s datasets. \| \| Dictionaries for Movies and Finance : \| Specific dictionaries for sentiment analysis using a specific field for testing data. Entries are clean and arranged in positive or negative connotations. \| \| OpinRank Dataset : \| 300,000 reviews from Edmunds and TripAdvisor. They’re neatly arranged by car model or by travel destination and relevant to the hotel. \| Text \| \| \| 20 Newsgroups : \| 20,000 documents from over 20 different newsgroups. The content covers a variety of topics with some closely related for reference. There are three versions, one in its original form, one with dates removed, and one with duplicates removed. \| \| The WikiQA Corpus : \| Contains question and sentence pairs. It’s robust and compiled from Bing query logs. There are over 3000 questions and over 29,000 answer sentences with just under 1500 labeled as answer sentences. \| \| European Parliament Proceedings Parallel Corpus : \| Sentence pairs from Parliament proceedings. There are entries from 21 European languages including some less common entries for ML corpus. \| \| Jeopardy : \| Over 200,000 questions from the famed tv show. It includes category and value designations as well as other descriptors like question and answer fields and rounds. \| \| Legal Case Reports Dataset : \| Text summaries of legal cases. It contains wrapups of over 4000 legal cases and could be great for training for automatic text summarization. \| Speech \| \| \| LibriSpeech : \| Nearly 1000 hours of speech in English taken from audiobook clips. \| \| Spoken Wikipedia Corpora : \| Spoken articles from Wikipedia in three languages, English, German, and Dutch. It includes a diverse speaker set and range of topics. There are hundreds of hours available for training sets. \| \| LJ Speech Dataset : \| 13,100 clips of short passages from audiobooks. They vary in length but contain a single speaker and include a transcription of the audio, which has been verified by a human reader. \| \| M-AI Labs Speech Dataset : \| Nearly 1000 hours of audio plus transcriptions. It includes multiple languages arranged by male voices, female voices, and a mix of the two. \| Neural Natural Language processing utilize deep neural network machine learning that results in enhanced language modeling and parsing. The majority of natural language processing solutions developed in the last 10 years generally use Neural NLP. The use of Neural networks greatly improves the ability of the capability of a NLP system to model, learn and reason. While also greatly reducing the amount of human perpetration of these systems. Key Natural Language concepts NLP Modeling – The modeling of natural language can refer to a number of different aspects such as encoding and decoding a sentence, creating sequence of labels. Using modern neural network in NLP this type of modeling allows for the breakdown of language into millions of trainable nodes that can preform syntactical semantic and sentiment analysis, language translation, topic extraction, classification and next sentence prediction. NLP Reasoning – Also known as common-sense reasoning is ability to allow computer to better interact and understand human interaction by collecting assumptions and extracting meaning behind the text provided but refining the assumptions throughout the duration of the conversation. In many ways common sense is the application of pragmatics in a conversational. Being situational aware of the context of the conversation is extremely important to interpreting meaning. This area of NLP, focusing on trying to reduce the “absurd” mistakes that NLP systems can make when they “jump to the wrong conclusion” due to a lack of commonsense. Natural Language Generation – This is the process by which the the machine learning algorithm creates natural language in such a way that is indistinguishable from a human response. This relies on constructing proper sentences that are suitable for the target demographics of users, without sounding to rigid or robotic. The key is to properly format and present the required response back to the user there are many ways to achive this one of the most common was is using statistical responses based from a large corpus of human text. Overview of a modern Conversational AI system. Natural Language Processing is an important part of a conversational AI system. It is this processing that is used to review the user’s input and prepare a reply back. Often conversational systems use voice interactions. In these cases the NLP is surrounded by a speech-to-text and text-to-speech process that is used to interact directly using voice. These voice replies utilize a synthetic voice engine which is modeled after the human voice. The goal of the synthesized voice is to provide a clear and easy to understand voice along while also attempting to mimic human speech inflections. \| Speech to Text Used to decode sound waves translate this into a textual representation of exactly what has been said. \| Natural Language Processing Evaluates the provided input, determines the correct response and formats the reply into natural language. \| Text to Speech The computer will formulate a response and translate a written text string into and auditory signal.. \| Natural Language is a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language.	pressbooks	2025-03-22T05:08:51.785566	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/#chapter-22-section-4", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/#chapter-22-section-5	Conversational AI Theory 2 Natural Language Processing Developing a computer application that can accurately parse and interpret how humans communicate has been a decades long struggle. Natural language is defined as a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language. The English language has around 600,000 words, within these words are complex grammatical constructs, different meanings and interpretations. This makes trying to develop a rule-based system that is capable of interpreting and speaking natural language next to impossible. History of Natural Language Processing Natural language processing is the technique that is used to decode, interpret and understand natural language. There have been essentially three generations of Virtual Language processing strategies that conversation all systems. These generations are Symbolic, Statistical and Neural. Symbolic Natural Language Processing was first introduced in the 1950s. Symbolic NLP functions by having a list of rules that the computer can use to evaluate and process language data. Symbolic systems were often used to solve language translation, as well as preform entity substitution into user utterances. For example a user might say “My hand hurts.” The chat bot could then reply with something like “Why does your hand hurt?” Unfortunately these types of systems lacked any contextual awareness. Statistical NLP By the 1990s the majority of conversational systems had moved to using statistical analysis of natural language. This type of processing required increased computing resources. The shift from Symbolic NLP to statistical represented a shift from fixed rules to probabilistic AI processes. These solutions relied on what is known as text corpora which is a sample of data (actual language) that contains meaningful context. This data can then be used to preform statistical analysis, hypothesis testing or validating linguistic rules. A corpus is a representative sample of actual language production within a meaningful context and with a general purpose. This can be thought of as training data for machine learning system. Source: https://odsc.medium.com/20-open-datasets-for-natural-language-processing-538fbfaf8e38 General \| \| \| Enron Dataset: \| Over half a million anonymized emails from over 100 users. It’s one of the few publicly available collections of “real” emails available for study and training sets. \| \| e Blogger Corpus: \| Nearly 700,000 blog posts from blogger.com. The meat of the blogs contain commonly occurring English words, at least 200 of them in each entry. \| \| SMS Spam Collection : \| Excellent dataset focused on spam. Nearly 6000 messages tagged as legitimate or spam messages with a useful subset extracted directly from Grumbletext. \| \| Recommender Systems Datasets : \| Datasets from a variety of sources, including fitness tracking, video games, song data, and social media. Labels include star ratings, time stamps, social networks, and images. \| \| Project Gutenberg : \| Extensive collection of book texts. These are public domain and available in a variety of languages, spanning a long period of time.\ \| \| Sentiment 140 : \| 160,000 tweets scrubbed of emoticons. They’re arranged in six fields — polarity, tweet date, user, text, query, and ID. \| \| MultiDomain Sentiment Analysis Dataset : \| Includes a wide range of Amazon reviews. Dataset can be converted to binary labels based on star review, and some product categories have thousands of entries. \| \| Yelp Reviews : \| Restaurant rankings and reviews. It includes a variety of aspects including reviews for sentiment analysis plus a challenge with cash prizes for those working with Yelp’s datasets. \| \| Dictionaries for Movies and Finance : \| Specific dictionaries for sentiment analysis using a specific field for testing data. Entries are clean and arranged in positive or negative connotations. \| \| OpinRank Dataset : \| 300,000 reviews from Edmunds and TripAdvisor. They’re neatly arranged by car model or by travel destination and relevant to the hotel. \| Text \| \| \| 20 Newsgroups : \| 20,000 documents from over 20 different newsgroups. The content covers a variety of topics with some closely related for reference. There are three versions, one in its original form, one with dates removed, and one with duplicates removed. \| \| The WikiQA Corpus : \| Contains question and sentence pairs. It’s robust and compiled from Bing query logs. There are over 3000 questions and over 29,000 answer sentences with just under 1500 labeled as answer sentences. \| \| European Parliament Proceedings Parallel Corpus : \| Sentence pairs from Parliament proceedings. There are entries from 21 European languages including some less common entries for ML corpus. \| \| Jeopardy : \| Over 200,000 questions from the famed tv show. It includes category and value designations as well as other descriptors like question and answer fields and rounds. \| \| Legal Case Reports Dataset : \| Text summaries of legal cases. It contains wrapups of over 4000 legal cases and could be great for training for automatic text summarization. \| Speech \| \| \| LibriSpeech : \| Nearly 1000 hours of speech in English taken from audiobook clips. \| \| Spoken Wikipedia Corpora : \| Spoken articles from Wikipedia in three languages, English, German, and Dutch. It includes a diverse speaker set and range of topics. There are hundreds of hours available for training sets. \| \| LJ Speech Dataset : \| 13,100 clips of short passages from audiobooks. They vary in length but contain a single speaker and include a transcription of the audio, which has been verified by a human reader. \| \| M-AI Labs Speech Dataset : \| Nearly 1000 hours of audio plus transcriptions. It includes multiple languages arranged by male voices, female voices, and a mix of the two. \| Neural Natural Language processing utilize deep neural network machine learning that results in enhanced language modeling and parsing. The majority of natural language processing solutions developed in the last 10 years generally use Neural NLP. The use of Neural networks greatly improves the ability of the capability of a NLP system to model, learn and reason. While also greatly reducing the amount of human perpetration of these systems. Key Natural Language concepts NLP Modeling – The modeling of natural language can refer to a number of different aspects such as encoding and decoding a sentence, creating sequence of labels. Using modern neural network in NLP this type of modeling allows for the breakdown of language into millions of trainable nodes that can preform syntactical semantic and sentiment analysis, language translation, topic extraction, classification and next sentence prediction. NLP Reasoning – Also known as common-sense reasoning is ability to allow computer to better interact and understand human interaction by collecting assumptions and extracting meaning behind the text provided but refining the assumptions throughout the duration of the conversation. In many ways common sense is the application of pragmatics in a conversational. Being situational aware of the context of the conversation is extremely important to interpreting meaning. This area of NLP, focusing on trying to reduce the “absurd” mistakes that NLP systems can make when they “jump to the wrong conclusion” due to a lack of commonsense. Natural Language Generation – This is the process by which the the machine learning algorithm creates natural language in such a way that is indistinguishable from a human response. This relies on constructing proper sentences that are suitable for the target demographics of users, without sounding to rigid or robotic. The key is to properly format and present the required response back to the user there are many ways to achive this one of the most common was is using statistical responses based from a large corpus of human text. Overview of a modern Conversational AI system. Natural Language Processing is an important part of a conversational AI system. It is this processing that is used to review the user’s input and prepare a reply back. Often conversational systems use voice interactions. In these cases the NLP is surrounded by a speech-to-text and text-to-speech process that is used to interact directly using voice. These voice replies utilize a synthetic voice engine which is modeled after the human voice. The goal of the synthesized voice is to provide a clear and easy to understand voice along while also attempting to mimic human speech inflections. \| Speech to Text Used to decode sound waves translate this into a textual representation of exactly what has been said. \| Natural Language Processing Evaluates the provided input, determines the correct response and formats the reply into natural language. \| Text to Speech The computer will formulate a response and translate a written text string into and auditory signal.. \| Natural Language is a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language.	pressbooks	2025-03-22T05:08:51.803881	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/#chapter-22-section-5", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/#chapter-22-section-6	Conversational AI Theory 2 Natural Language Processing Developing a computer application that can accurately parse and interpret how humans communicate has been a decades long struggle. Natural language is defined as a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language. The English language has around 600,000 words, within these words are complex grammatical constructs, different meanings and interpretations. This makes trying to develop a rule-based system that is capable of interpreting and speaking natural language next to impossible. History of Natural Language Processing Natural language processing is the technique that is used to decode, interpret and understand natural language. There have been essentially three generations of Virtual Language processing strategies that conversation all systems. These generations are Symbolic, Statistical and Neural. Symbolic Natural Language Processing was first introduced in the 1950s. Symbolic NLP functions by having a list of rules that the computer can use to evaluate and process language data. Symbolic systems were often used to solve language translation, as well as preform entity substitution into user utterances. For example a user might say “My hand hurts.” The chat bot could then reply with something like “Why does your hand hurt?” Unfortunately these types of systems lacked any contextual awareness. Statistical NLP By the 1990s the majority of conversational systems had moved to using statistical analysis of natural language. This type of processing required increased computing resources. The shift from Symbolic NLP to statistical represented a shift from fixed rules to probabilistic AI processes. These solutions relied on what is known as text corpora which is a sample of data (actual language) that contains meaningful context. This data can then be used to preform statistical analysis, hypothesis testing or validating linguistic rules. A corpus is a representative sample of actual language production within a meaningful context and with a general purpose. This can be thought of as training data for machine learning system. Source: https://odsc.medium.com/20-open-datasets-for-natural-language-processing-538fbfaf8e38 General \| \| \| Enron Dataset: \| Over half a million anonymized emails from over 100 users. It’s one of the few publicly available collections of “real” emails available for study and training sets. \| \| e Blogger Corpus: \| Nearly 700,000 blog posts from blogger.com. The meat of the blogs contain commonly occurring English words, at least 200 of them in each entry. \| \| SMS Spam Collection : \| Excellent dataset focused on spam. Nearly 6000 messages tagged as legitimate or spam messages with a useful subset extracted directly from Grumbletext. \| \| Recommender Systems Datasets : \| Datasets from a variety of sources, including fitness tracking, video games, song data, and social media. Labels include star ratings, time stamps, social networks, and images. \| \| Project Gutenberg : \| Extensive collection of book texts. These are public domain and available in a variety of languages, spanning a long period of time.\ \| \| Sentiment 140 : \| 160,000 tweets scrubbed of emoticons. They’re arranged in six fields — polarity, tweet date, user, text, query, and ID. \| \| MultiDomain Sentiment Analysis Dataset : \| Includes a wide range of Amazon reviews. Dataset can be converted to binary labels based on star review, and some product categories have thousands of entries. \| \| Yelp Reviews : \| Restaurant rankings and reviews. It includes a variety of aspects including reviews for sentiment analysis plus a challenge with cash prizes for those working with Yelp’s datasets. \| \| Dictionaries for Movies and Finance : \| Specific dictionaries for sentiment analysis using a specific field for testing data. Entries are clean and arranged in positive or negative connotations. \| \| OpinRank Dataset : \| 300,000 reviews from Edmunds and TripAdvisor. They’re neatly arranged by car model or by travel destination and relevant to the hotel. \| Text \| \| \| 20 Newsgroups : \| 20,000 documents from over 20 different newsgroups. The content covers a variety of topics with some closely related for reference. There are three versions, one in its original form, one with dates removed, and one with duplicates removed. \| \| The WikiQA Corpus : \| Contains question and sentence pairs. It’s robust and compiled from Bing query logs. There are over 3000 questions and over 29,000 answer sentences with just under 1500 labeled as answer sentences. \| \| European Parliament Proceedings Parallel Corpus : \| Sentence pairs from Parliament proceedings. There are entries from 21 European languages including some less common entries for ML corpus. \| \| Jeopardy : \| Over 200,000 questions from the famed tv show. It includes category and value designations as well as other descriptors like question and answer fields and rounds. \| \| Legal Case Reports Dataset : \| Text summaries of legal cases. It contains wrapups of over 4000 legal cases and could be great for training for automatic text summarization. \| Speech \| \| \| LibriSpeech : \| Nearly 1000 hours of speech in English taken from audiobook clips. \| \| Spoken Wikipedia Corpora : \| Spoken articles from Wikipedia in three languages, English, German, and Dutch. It includes a diverse speaker set and range of topics. There are hundreds of hours available for training sets. \| \| LJ Speech Dataset : \| 13,100 clips of short passages from audiobooks. They vary in length but contain a single speaker and include a transcription of the audio, which has been verified by a human reader. \| \| M-AI Labs Speech Dataset : \| Nearly 1000 hours of audio plus transcriptions. It includes multiple languages arranged by male voices, female voices, and a mix of the two. \| Neural Natural Language processing utilize deep neural network machine learning that results in enhanced language modeling and parsing. The majority of natural language processing solutions developed in the last 10 years generally use Neural NLP. The use of Neural networks greatly improves the ability of the capability of a NLP system to model, learn and reason. While also greatly reducing the amount of human perpetration of these systems. Key Natural Language concepts NLP Modeling – The modeling of natural language can refer to a number of different aspects such as encoding and decoding a sentence, creating sequence of labels. Using modern neural network in NLP this type of modeling allows for the breakdown of language into millions of trainable nodes that can preform syntactical semantic and sentiment analysis, language translation, topic extraction, classification and next sentence prediction. NLP Reasoning – Also known as common-sense reasoning is ability to allow computer to better interact and understand human interaction by collecting assumptions and extracting meaning behind the text provided but refining the assumptions throughout the duration of the conversation. In many ways common sense is the application of pragmatics in a conversational. Being situational aware of the context of the conversation is extremely important to interpreting meaning. This area of NLP, focusing on trying to reduce the “absurd” mistakes that NLP systems can make when they “jump to the wrong conclusion” due to a lack of commonsense. Natural Language Generation – This is the process by which the the machine learning algorithm creates natural language in such a way that is indistinguishable from a human response. This relies on constructing proper sentences that are suitable for the target demographics of users, without sounding to rigid or robotic. The key is to properly format and present the required response back to the user there are many ways to achive this one of the most common was is using statistical responses based from a large corpus of human text. Overview of a modern Conversational AI system. Natural Language Processing is an important part of a conversational AI system. It is this processing that is used to review the user’s input and prepare a reply back. Often conversational systems use voice interactions. In these cases the NLP is surrounded by a speech-to-text and text-to-speech process that is used to interact directly using voice. These voice replies utilize a synthetic voice engine which is modeled after the human voice. The goal of the synthesized voice is to provide a clear and easy to understand voice along while also attempting to mimic human speech inflections. \| Speech to Text Used to decode sound waves translate this into a textual representation of exactly what has been said. \| Natural Language Processing Evaluates the provided input, determines the correct response and formats the reply into natural language. \| Text to Speech The computer will formulate a response and translate a written text string into and auditory signal.. \| Natural Language is a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language.	pressbooks	2025-03-22T05:08:51.823802	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/#chapter-22-section-6", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/bert/	Converstational AI Frameworks 3 BERT BERT stands for Bidirectional Encoder Representation form Transformers and was developed in 2018 by Google. This NLP technique utilized pre-trained transformer type neural networks. There are different sizes of the BERT model to meet various use-cases and available computing resources for different applications. The model size does influence the the accuracy of the predictions. In general having a larger model size results in a more accurate result. However, larger models consume more processing power and take longer to process. In the case of conversation AI processing delays can greatly impact the user experience and studies have shown that in a typical natural language conversation with the average delay in between exchanges is 300ms. This is a very narrow window of time to evaluate the intent of the user, fetch any external data that is required and prepare a response. When you are running multiple models against a query you may only have 10ms to evaluate and decode the natural language. [1] According to Mohd Sanad Zaki Rizvi[2] TinyBERT model to achieve 96% of its BERT base teacher on the BLUE benchmark while being 7.5x smaller and 9.4x faster! Its performance numbers are impressive even when comparing with BERT small, a model of exactly the same size, which TinyBERT is 9% better than (76.5 vs 70.2 points average on GLUE). \| Model \| Parameters \| Layers \| Hidden \| \| BERT Tiny \| 4M \| 2 \| 128 \| \| BERTMini \| 11M \| 4 \| 256 \| \| BERT Small \| 29M \| 4 \| 512 \| \| BERT Medium \| 42M \| 8 \| 512 \| \| BERT Base \| 108M \| 12 \| 768 \| \| BERT large \| 334M \| 24 \| 1024 \| \| BERT. xlarge \| 1270M \| 24 \| 2048 \| \| ALBERT base \| 12M \| 12 \| 768 \| \| ALBERT large \| 18M \| 24 \| 1024 \| \| ALBERT xlarge \| 59M \| 24 \| 2048 \| \| ALBERT xxlarge \| 233M \| 12 \| 4096 \|	pressbooks	2025-03-22T05:08:51.836176	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/bert/", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/gpt/	Converstational AI Frameworks 4 GPT GPT stands for Generative Pre-trained Transformer and as of January 2022 is in it’s third generation. GPT is an auto-regressive language model that uses deep learning to produce human-like text. GPT-3 utilizes a total of 175 billion of parameters it is one of the largest publicly available language models available. Although the model is now owned by Microsoft there are a number of API interfaces to the model for a varieties of uses. In general GTP-3 is advertised to be able to be “applied to virtually any task that involves understanding or generating natural language or code. We offer a spectrum of models with different levels of power suitable for different tasks, as well as the ability to fine-tune your own custom models. These models can be used for everything from content generation to semantic search and classification.”[1] GPT-3 Key Features In general there are 4 key features of GPT-3. These are - Completion - Classification - Search According to the GPT-3 Documentation[2] there are number of API endpoints that can be accessed within each of these three key features. Completion Completion provides a simple but powerful interface whereby the user provides some textual input as a prompt, and the model will generate a text completion that attempts to match whatever context or pattern you gave it. Generation – One of the most powerful yet simplest tasks you can accomplish with the API is generating new ideas or versions of input. You can ask for anything from story ideas, to business plans, to character descriptions and marketing slogans. In this example, we’ll use the API to create ideas for using virtual reality in fitness. Conversation – The API is extremely adept at carrying on conversations with humans and even with itself. With just a few lines of instruction, we’ve seen the API perform as a customer service chatbot that intelligently answers questions without ever getting flustered or a wise-cracking conversation partner that makes jokes and puns. The key is to tell the API how it should behave and then provide a few examples. Transformation – The API is a language model that is familiar with a variety of ways that words and characters can be used to express information. This ranges from natural language text to code and languages other than English. The API is also able to understand content on a level that allows it to summarize, convert and express it in different ways. Summarization – The API is able to grasp the context of text and rephrase it in different ways. In this example, we create an explanation a child would understand from a longer, more sophisticated text passage. This illustrates that the API has a deep grasp of language. Factual responses – The API has a lot of knowledge that it’s learned from the data that it was been trained on. It also has the ability to provide responses that sound very real but are in fact made up. There are two ways to limit the likelihood of the API making up an answer. Classification Can be used to evaluate future data against a number of criteria such as positive and negative. The Classifications endpoints provides the ability to leverage a labeled set of examples without fine-tuning and can be used for any text-to-label task. By avoiding fine-tuning, it eliminates the need for hyper-parameter tuning. The endpoint serves as an “autoML” solution that is easy to configure, and adapt to changing label schema. Up to 200 labeled examples or a pre-uploaded file can be provided at query time. Search The Search endpoint allows you to do a semantic search over a set of documents. This means that you can provide a query, such as a natural language question or a statement, and the provided documents will be scored and ranked based on how semantically related they are to the input query.	pressbooks	2025-03-22T05:08:51.848214	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/gpt/", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/gpt/#chapter-47-section-1	Converstational AI Frameworks 4 GPT GPT stands for Generative Pre-trained Transformer and as of January 2022 is in it’s third generation. GPT is an auto-regressive language model that uses deep learning to produce human-like text. GPT-3 utilizes a total of 175 billion of parameters it is one of the largest publicly available language models available. Although the model is now owned by Microsoft there are a number of API interfaces to the model for a varieties of uses. In general GTP-3 is advertised to be able to be “applied to virtually any task that involves understanding or generating natural language or code. We offer a spectrum of models with different levels of power suitable for different tasks, as well as the ability to fine-tune your own custom models. These models can be used for everything from content generation to semantic search and classification.”[1] GPT-3 Key Features In general there are 4 key features of GPT-3. These are - Completion - Classification - Search According to the GPT-3 Documentation[2] there are number of API endpoints that can be accessed within each of these three key features. Completion Completion provides a simple but powerful interface whereby the user provides some textual input as a prompt, and the model will generate a text completion that attempts to match whatever context or pattern you gave it. Generation – One of the most powerful yet simplest tasks you can accomplish with the API is generating new ideas or versions of input. You can ask for anything from story ideas, to business plans, to character descriptions and marketing slogans. In this example, we’ll use the API to create ideas for using virtual reality in fitness. Conversation – The API is extremely adept at carrying on conversations with humans and even with itself. With just a few lines of instruction, we’ve seen the API perform as a customer service chatbot that intelligently answers questions without ever getting flustered or a wise-cracking conversation partner that makes jokes and puns. The key is to tell the API how it should behave and then provide a few examples. Transformation – The API is a language model that is familiar with a variety of ways that words and characters can be used to express information. This ranges from natural language text to code and languages other than English. The API is also able to understand content on a level that allows it to summarize, convert and express it in different ways. Summarization – The API is able to grasp the context of text and rephrase it in different ways. In this example, we create an explanation a child would understand from a longer, more sophisticated text passage. This illustrates that the API has a deep grasp of language. Factual responses – The API has a lot of knowledge that it’s learned from the data that it was been trained on. It also has the ability to provide responses that sound very real but are in fact made up. There are two ways to limit the likelihood of the API making up an answer. Classification Can be used to evaluate future data against a number of criteria such as positive and negative. The Classifications endpoints provides the ability to leverage a labeled set of examples without fine-tuning and can be used for any text-to-label task. By avoiding fine-tuning, it eliminates the need for hyper-parameter tuning. The endpoint serves as an “autoML” solution that is easy to configure, and adapt to changing label schema. Up to 200 labeled examples or a pre-uploaded file can be provided at query time. Search The Search endpoint allows you to do a semantic search over a set of documents. This means that you can provide a query, such as a natural language question or a statement, and the provided documents will be scored and ranked based on how semantically related they are to the input query.	pressbooks	2025-03-22T05:08:51.859369	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/gpt/#chapter-47-section-1", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/gpt/#chapter-47-section-2	Converstational AI Frameworks 4 GPT GPT stands for Generative Pre-trained Transformer and as of January 2022 is in it’s third generation. GPT is an auto-regressive language model that uses deep learning to produce human-like text. GPT-3 utilizes a total of 175 billion of parameters it is one of the largest publicly available language models available. Although the model is now owned by Microsoft there are a number of API interfaces to the model for a varieties of uses. In general GTP-3 is advertised to be able to be “applied to virtually any task that involves understanding or generating natural language or code. We offer a spectrum of models with different levels of power suitable for different tasks, as well as the ability to fine-tune your own custom models. These models can be used for everything from content generation to semantic search and classification.”[1] GPT-3 Key Features In general there are 4 key features of GPT-3. These are - Completion - Classification - Search According to the GPT-3 Documentation[2] there are number of API endpoints that can be accessed within each of these three key features. Completion Completion provides a simple but powerful interface whereby the user provides some textual input as a prompt, and the model will generate a text completion that attempts to match whatever context or pattern you gave it. Generation – One of the most powerful yet simplest tasks you can accomplish with the API is generating new ideas or versions of input. You can ask for anything from story ideas, to business plans, to character descriptions and marketing slogans. In this example, we’ll use the API to create ideas for using virtual reality in fitness. Conversation – The API is extremely adept at carrying on conversations with humans and even with itself. With just a few lines of instruction, we’ve seen the API perform as a customer service chatbot that intelligently answers questions without ever getting flustered or a wise-cracking conversation partner that makes jokes and puns. The key is to tell the API how it should behave and then provide a few examples. Transformation – The API is a language model that is familiar with a variety of ways that words and characters can be used to express information. This ranges from natural language text to code and languages other than English. The API is also able to understand content on a level that allows it to summarize, convert and express it in different ways. Summarization – The API is able to grasp the context of text and rephrase it in different ways. In this example, we create an explanation a child would understand from a longer, more sophisticated text passage. This illustrates that the API has a deep grasp of language. Factual responses – The API has a lot of knowledge that it’s learned from the data that it was been trained on. It also has the ability to provide responses that sound very real but are in fact made up. There are two ways to limit the likelihood of the API making up an answer. Classification Can be used to evaluate future data against a number of criteria such as positive and negative. The Classifications endpoints provides the ability to leverage a labeled set of examples without fine-tuning and can be used for any text-to-label task. By avoiding fine-tuning, it eliminates the need for hyper-parameter tuning. The endpoint serves as an “autoML” solution that is easy to configure, and adapt to changing label schema. Up to 200 labeled examples or a pre-uploaded file can be provided at query time. Search The Search endpoint allows you to do a semantic search over a set of documents. This means that you can provide a query, such as a natural language question or a statement, and the provided documents will be scored and ranked based on how semantically related they are to the input query.	pressbooks	2025-03-22T05:08:51.870259	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/gpt/#chapter-47-section-2", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/gpt/#chapter-47-section-3	Converstational AI Frameworks 4 GPT GPT stands for Generative Pre-trained Transformer and as of January 2022 is in it’s third generation. GPT is an auto-regressive language model that uses deep learning to produce human-like text. GPT-3 utilizes a total of 175 billion of parameters it is one of the largest publicly available language models available. Although the model is now owned by Microsoft there are a number of API interfaces to the model for a varieties of uses. In general GTP-3 is advertised to be able to be “applied to virtually any task that involves understanding or generating natural language or code. We offer a spectrum of models with different levels of power suitable for different tasks, as well as the ability to fine-tune your own custom models. These models can be used for everything from content generation to semantic search and classification.”[1] GPT-3 Key Features In general there are 4 key features of GPT-3. These are - Completion - Classification - Search According to the GPT-3 Documentation[2] there are number of API endpoints that can be accessed within each of these three key features. Completion Completion provides a simple but powerful interface whereby the user provides some textual input as a prompt, and the model will generate a text completion that attempts to match whatever context or pattern you gave it. Generation – One of the most powerful yet simplest tasks you can accomplish with the API is generating new ideas or versions of input. You can ask for anything from story ideas, to business plans, to character descriptions and marketing slogans. In this example, we’ll use the API to create ideas for using virtual reality in fitness. Conversation – The API is extremely adept at carrying on conversations with humans and even with itself. With just a few lines of instruction, we’ve seen the API perform as a customer service chatbot that intelligently answers questions without ever getting flustered or a wise-cracking conversation partner that makes jokes and puns. The key is to tell the API how it should behave and then provide a few examples. Transformation – The API is a language model that is familiar with a variety of ways that words and characters can be used to express information. This ranges from natural language text to code and languages other than English. The API is also able to understand content on a level that allows it to summarize, convert and express it in different ways. Summarization – The API is able to grasp the context of text and rephrase it in different ways. In this example, we create an explanation a child would understand from a longer, more sophisticated text passage. This illustrates that the API has a deep grasp of language. Factual responses – The API has a lot of knowledge that it’s learned from the data that it was been trained on. It also has the ability to provide responses that sound very real but are in fact made up. There are two ways to limit the likelihood of the API making up an answer. Classification Can be used to evaluate future data against a number of criteria such as positive and negative. The Classifications endpoints provides the ability to leverage a labeled set of examples without fine-tuning and can be used for any text-to-label task. By avoiding fine-tuning, it eliminates the need for hyper-parameter tuning. The endpoint serves as an “autoML” solution that is easy to configure, and adapt to changing label schema. Up to 200 labeled examples or a pre-uploaded file can be provided at query time. Search The Search endpoint allows you to do a semantic search over a set of documents. This means that you can provide a query, such as a natural language question or a statement, and the provided documents will be scored and ranked based on how semantically related they are to the input query.	pressbooks	2025-03-22T05:08:51.881018	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/gpt/#chapter-47-section-3", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/gpt/#chapter-47-section-4	Converstational AI Frameworks 4 GPT GPT stands for Generative Pre-trained Transformer and as of January 2022 is in it’s third generation. GPT is an auto-regressive language model that uses deep learning to produce human-like text. GPT-3 utilizes a total of 175 billion of parameters it is one of the largest publicly available language models available. Although the model is now owned by Microsoft there are a number of API interfaces to the model for a varieties of uses. In general GTP-3 is advertised to be able to be “applied to virtually any task that involves understanding or generating natural language or code. We offer a spectrum of models with different levels of power suitable for different tasks, as well as the ability to fine-tune your own custom models. These models can be used for everything from content generation to semantic search and classification.”[1] GPT-3 Key Features In general there are 4 key features of GPT-3. These are - Completion - Classification - Search According to the GPT-3 Documentation[2] there are number of API endpoints that can be accessed within each of these three key features. Completion Completion provides a simple but powerful interface whereby the user provides some textual input as a prompt, and the model will generate a text completion that attempts to match whatever context or pattern you gave it. Generation – One of the most powerful yet simplest tasks you can accomplish with the API is generating new ideas or versions of input. You can ask for anything from story ideas, to business plans, to character descriptions and marketing slogans. In this example, we’ll use the API to create ideas for using virtual reality in fitness. Conversation – The API is extremely adept at carrying on conversations with humans and even with itself. With just a few lines of instruction, we’ve seen the API perform as a customer service chatbot that intelligently answers questions without ever getting flustered or a wise-cracking conversation partner that makes jokes and puns. The key is to tell the API how it should behave and then provide a few examples. Transformation – The API is a language model that is familiar with a variety of ways that words and characters can be used to express information. This ranges from natural language text to code and languages other than English. The API is also able to understand content on a level that allows it to summarize, convert and express it in different ways. Summarization – The API is able to grasp the context of text and rephrase it in different ways. In this example, we create an explanation a child would understand from a longer, more sophisticated text passage. This illustrates that the API has a deep grasp of language. Factual responses – The API has a lot of knowledge that it’s learned from the data that it was been trained on. It also has the ability to provide responses that sound very real but are in fact made up. There are two ways to limit the likelihood of the API making up an answer. Classification Can be used to evaluate future data against a number of criteria such as positive and negative. The Classifications endpoints provides the ability to leverage a labeled set of examples without fine-tuning and can be used for any text-to-label task. By avoiding fine-tuning, it eliminates the need for hyper-parameter tuning. The endpoint serves as an “autoML” solution that is easy to configure, and adapt to changing label schema. Up to 200 labeled examples or a pre-uploaded file can be provided at query time. Search The Search endpoint allows you to do a semantic search over a set of documents. This means that you can provide a query, such as a natural language question or a statement, and the provided documents will be scored and ranked based on how semantically related they are to the input query.	pressbooks	2025-03-22T05:08:51.891857	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/gpt/#chapter-47-section-4", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/natural-language-platforms/	Converstational AI Frameworks 5 Natural Language Platforms Using an established Natural Language Platforms make it easier for developers to design and deploy conversational systems to users as well as helps developers reach more users and also the capability to interface to Internet of Things (IoT) devices. Most natural language processing platforms today handle the two key aspects related to natural language processing and understanding. They more or less all follow a similar methodology whereby they interpret a users utterance, then map it against intents that are created in order to respond to the user’s utterance. These Conversational Language Platform benefit from having a huge amount of computing resources powering the back-end which is therefore capable of supporting a larger language model, as well as benefit from having a diverse language set from a wide range of users from around the world. As a result these web service frameworks tend to offer improved natural language understanding capabilities when compared with a stand-alone system. Each of these natural language platforms offer similar services, however how they are constructed, function and interact with other systems vary from platform to platform. Amazon Lex Amazon Lex is a fully managed artificial intelligence (AI) service with advanced natural language models to design, build, test, and deploy conversational interfaces in applications.The Alexa Skills kit utilizes Lex and allows developers to “teach” Alexa new skills. Users access these new abilities by asking Alexa questions or making requests.These short interactions, are meant to expedite human-computer interaction. IBM Watson Assistant Watson Assistant is a chatbot that provides exceptional customer service. We call it a virtual assistant because it’s much more than just an FAQ wrapped in a personality. Watson Assistant gives fast, consistent, and accurate answers across any application, device, or channel (including voice). Using artificial intelligence (AI), Watson Assistant is able to learn from customer conversations, improving its ability to resolve issues the first time and working to keep your customers from getting frustrated. Google Dialogflow Dialogflow is a natural language understanding platform that makes it easy to design and integrate a conversational user interface into mobile apps, web application, device, bot and interactive voice response systems. Dialogflow can provide new and engaging ways for users to interact with existing systems and can analyze multiple types of input from users including text or audio inputs (phone or voice recording). It can also respond either through text or with synthetic speech. Meta Wit Wit is a natural Language Platform developed by Meta (Formerly Facebook). We are building the AI platform that helps 200,000+ developers create apps that understand human language. That’s an ambitious goal and our approach is different: We provide developers everywhere with a simple way to build apps that understand text and voice commands, and learn from every interaction. We leverage the community: what Wit.ai learns is shared among developers. Microsoft LUIS Designed to identify valuable information in conversations, LUIS interprets user goals (intents) and distills valuable information from sentences (entities), for a high quality, nuanced language model. LUIS integrates seamlessly with the Azure Bot Service, making it easy to create a sophisticated bot.	pressbooks	2025-03-22T05:08:51.905373	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/natural-language-platforms/", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/natural-language-platforms/#chapter-84-section-1	Converstational AI Frameworks 5 Natural Language Platforms Using an established Natural Language Platforms make it easier for developers to design and deploy conversational systems to users as well as helps developers reach more users and also the capability to interface to Internet of Things (IoT) devices. Most natural language processing platforms today handle the two key aspects related to natural language processing and understanding. They more or less all follow a similar methodology whereby they interpret a users utterance, then map it against intents that are created in order to respond to the user’s utterance. These Conversational Language Platform benefit from having a huge amount of computing resources powering the back-end which is therefore capable of supporting a larger language model, as well as benefit from having a diverse language set from a wide range of users from around the world. As a result these web service frameworks tend to offer improved natural language understanding capabilities when compared with a stand-alone system. Each of these natural language platforms offer similar services, however how they are constructed, function and interact with other systems vary from platform to platform. Amazon Lex Amazon Lex is a fully managed artificial intelligence (AI) service with advanced natural language models to design, build, test, and deploy conversational interfaces in applications.The Alexa Skills kit utilizes Lex and allows developers to “teach” Alexa new skills. Users access these new abilities by asking Alexa questions or making requests.These short interactions, are meant to expedite human-computer interaction. IBM Watson Assistant Watson Assistant is a chatbot that provides exceptional customer service. We call it a virtual assistant because it’s much more than just an FAQ wrapped in a personality. Watson Assistant gives fast, consistent, and accurate answers across any application, device, or channel (including voice). Using artificial intelligence (AI), Watson Assistant is able to learn from customer conversations, improving its ability to resolve issues the first time and working to keep your customers from getting frustrated. Google Dialogflow Dialogflow is a natural language understanding platform that makes it easy to design and integrate a conversational user interface into mobile apps, web application, device, bot and interactive voice response systems. Dialogflow can provide new and engaging ways for users to interact with existing systems and can analyze multiple types of input from users including text or audio inputs (phone or voice recording). It can also respond either through text or with synthetic speech. Meta Wit Wit is a natural Language Platform developed by Meta (Formerly Facebook). We are building the AI platform that helps 200,000+ developers create apps that understand human language. That’s an ambitious goal and our approach is different: We provide developers everywhere with a simple way to build apps that understand text and voice commands, and learn from every interaction. We leverage the community: what Wit.ai learns is shared among developers. Microsoft LUIS Designed to identify valuable information in conversations, LUIS interprets user goals (intents) and distills valuable information from sentences (entities), for a high quality, nuanced language model. LUIS integrates seamlessly with the Azure Bot Service, making it easy to create a sophisticated bot.	pressbooks	2025-03-22T05:08:51.923100	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/natural-language-platforms/#chapter-84-section-1", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/diaglogflow-overview/	Google Dialogflow 6 Diaglogflow Overview To access Dialogflow you first need to have a Google account. Then you can navigate to the Dialogflow console which can be found here. https://dialogflow.cloud.google.com/ Once you are logged into the Dialogflow console (figure 1) you will be prompted to create a new agent. (figure 2) This agent represents your application and can be thought of as the personification of a human agent that will converse with your end users. You will be required to provide the following attributes as you create the project. - Agent Name - Default Language - Default Timezone. The default language in time zone are used unless overridden by the client. Note: You cannot change the default language after you create the agent. As of Janauary 2022, Dialogflow supports 55 language variations for text chats. It is important to note that not all languages are fully supported for all client types. Since Dialogflow is capable of a number of different client features that are listed below it is important to consult the Official Dialogflow Language table to make sure that your languages is supported for the client features that you are looking to use. Dialogflow Client Features - (text-only chat) - (speech-to-text, audio input, speech recognition) - (text-to-speech, audio output, speech synthesis) - (Dialogflow Phone Gateway) - (Knowledge Connectors) - (Sentiment Analysis) - (Built-in Small Talk) Dialogflow Menu The menu on the left side of the screen provides quick access to the majority of the Dialogflow features. Intents – This is where you will manage the intents of your agent. And intent a way of handling the end-users intention during a conversation. It could be thought of as a reason that they are conversing with your agent or as something they would like to do during the conversation. Entities – this is where you will manage the entities of your agent. Entities can be thought of as data variables that the user provides during conversation. Knowledge – the knowledge feature is used to connect to an external data source such as a webpage and can automatically create things like a set of frequently asked questions or knowledge base. Fulfillment – This is how Dialogflow connects to different back-end systems to extract information. Such as retrieving current weather conditions from a weather service API. Integrations- This is where your Digalogflow agent connects with other platforms such as Slack, Facebook Messenger, Twitter, Telegram or even the telephone system. The rest of this menu will be discussed in future chapters of this book.	pressbooks	2025-03-22T05:08:51.938527	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/diaglogflow-overview/", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://opentextbc.ca/adaptopentextbook/chapter/definition-of-adapt/	What is an Adaptation 1 Definition of Adapt The term adaptation is commonly used to describe the process of making changes to an existing work. Though we can also replace “adapt” with revise, modify, alter, customize, or other synonym that describes the act of making a change. In addition to cost to students, one of the biggest advantages of choosing an open textbook is it gives faculty the legal right to add to, adapt, or delete the content of the textbook to fit their specific course without obtaining permission from the copyright holder. This is possible because the copyright holder has already granted permission by releasing their work using an open licence, such as a Creative Commons licence. This type of licence gives users permission to use and reuse, share, copy, retain and modify the textbook without consulting the author.	pressbooks	2025-03-22T05:08:51.946970	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/definition-of-adapt/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/reasons-adapt/	Why Adapt an Open Textbook 2 Reasons to Adapt an Open Textbook One of the benefits of using an openly licensed textbook or other educational resource is that you are free to adapt it to fit your needs. In other words, you can adjust the educational resources to fit your course curriculum, not the other way around. Other reasons for revising an existing open work might be to: - Address a particular teaching style or learning style - Adjust for a different grade or course level - Adapt for a different discipline - Accommodate a different learning environment - Address diversity needs - Meet a cultural preference - Meet a regional or national preference - Address a school, district, or institution’s standardized curriculum - Make the material more accessible for people with disabilities - Add material contributed by students or material suggested by students - Translate the material into another language - Correct errors or inaccuracies - Update the book with current information - Add more media or links to other resources - Use only a portion of the book for a course[1] - Some of this material is based on: WikiEducator. “Adapt” in OER Handbook for Educators (http://wikieducator.org/OER_Handbook/educator_version_one/Adapt) and Why Remix Open Educational Resources? created by Liam Green-Hughes, both used under a CC-BY licence ↵	pressbooks	2025-03-22T05:08:51.959697	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/reasons-adapt/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/permission-to-adapt/	Why Adapt an Open Textbook 3 Permission to Adapt When it comes to working with open textbooks (and open educational resources in general), one of the conceptual hurdles faced by most people is around the notion of adapting or changing someone’s work. What exactly can be adapted within the scope of an open textbook, and won’t the original author get upset if you change their work? Changing someone’s work can feel uncomfortable. But rest assured, if the author has released their textbook under a Creative Commons licence that allows for adaptation (which is any Creative Commons licence that does not have a No Derivative (ND) attribute added to it) then they expect that you will change the content, providing you give them the proper attribution (and we’ll get into this). Using information and media from an open textbook or other open educational resource are NOT considered plagiarism. But what can you change? Anything and everything in an open textbook can be changed as long as the conditions of the open licence are met. The modifications or changes you make can be fairly minor or major depending on what you need to do to make the book work for you. That is the beauty and power of open textbooks. You are in charge of the resource. You have been given permission to change it ahead of time by the original author. Take advantage of it. They want you to. Why you should use openly licensed materials If you are looking for content to add to your textbook, you should look for and use Creative Commons licensed material (learn more in this appendix about Licences and Tools in the Self-Publishing Guide). While you can use material that has not been released under a Creative Commons licence, it does limit how others can use or reuse that material. As well, you must first obtain written permission from the copyright holder to use copyrighted material in the textbook and clearly note in the textbook the specific material that is copyrighted. This is to ensure others using the book in the future know they cannot reuse that material. Don’t forget the public domain Once the copyright of a work has expired, has been forfeited, or is inapplicable, it is part of the public domain, meaning that permission is not required to use it or make changes to it. Creative Commons provides public domain tools that enable authors and copyright owners who want to dedicate their works to the worldwide public domain to do so, and facilitates the labeling and discovery of works that are already free of known copyright restrictions. Note: Using copyright material released with a restrictive licence is a barrier to future reuse and limits the usage of the resource in the future. Therefore, we recommend using Creative Commons licensed material that can legally be shared and reused. See Choose a Licence.	pressbooks	2025-03-22T05:08:51.970940	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/permission-to-adapt/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/three-steps/	Why Adapt an Open Textbook 4 Three Steps Before You Begin A good rule of thumb when creating an adapted textbook is to keep it simple, especially if you are approaching a remix project for the first time. While it may be tempting to make a number of major changes to a textbook before releasing it to your students, think of the textbook as a living resource that you can improve incrementally over time. Here are three steps to consider before adapting an existing textbook. Step 1: Check the licence First, check the licence to make sure you have the permission to modify the contents. As long as the Creative Commons licence does not have a No Derivative (ND) attribute, you are able to change the contents of the book. See Creative Commons for more information on licences. Step 2: Check the file format If you want to adapt an open textbook, you will need it in a workable technical format, i.e. an editable file type. These include: - Pressbooks or WordPress files (.xml or .wxr) - HTML files (webpages) - Word document (.docx) or OpenDocument Text (.odt) - Simple text files (.txt) - EPUB (see “Export Google Docs files as EPUB publications“) - LaTeX files (if the original book includes math or science formulas and equations) Avoid PDF documents Many open textbooks are only available as a PDF document, which are not editable. If you want to adapt an open textbook that is only available in PDF format, you will need to convert the PDF document to one of the editable formats listed above. Converting a PDF document to an editable format is a difficult, time consuming, and imprecise process. Before taking the time to do this, consider contacting the author and asking for a copy of the textbook’s source files. Step 3: Use editing tools Once you have an editable file, you are ready to begin your adaptation. The tools you use to create your adaptation will depend on the source file of the original textbook and how comfortable you feel working with the format and tool. Pressbooks Pressbooks is a commonly used publishing and editing tool. You can import a number of different formats into Pressbooks for editing including.	pressbooks	2025-03-22T05:08:51.982613	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/three-steps/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/start-small/	Why Adapt an Open Textbook 5 Start Small An adaptation can turn ugly, like a house renovation project gone mad taking twice the time and three times the energy than you thought. To prevent from falling into this trap, consider starting small. For your first crack at an adaptation, decide to make a few minor changes such as: - Removing the chapters you don’t need OR - Adding an example or two from your current curriculum OR - Including some exercises you’ve found useful in the classroom. Start small. But think big. While you are taking these baby steps, think of the huge pedagogical potential hidden away in an open textbook. Plan and dream and scheme about what could be done with this book; a book in which the author has give you permission to use, share, and change content to your heart’s content and the only payment is to give her or him credit. What a gift! So, start small and don’t let this gift become a demanding, uncontrollable beast that consumes you. Start small, give it a few treats, and get to know each other. Then, when you’re bursting at the seams with ideas and enthusiasm, write down all of the wonderful things you want to change, when both you and the textbook are ready, on your “Adaptation Bucket List”.	pressbooks	2025-03-22T05:08:51.992002	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/start-small/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/already-available/	How to Adapt an Open Textbook 6 Is This Book Already Available? There are many open textbooks, both new creations and adaptations, currently in production and once finished will be available to the open education community. Before you consider adapting an existing open textbooks, review the following lists for open textbook projects currently in progress. \| Project \| List of Open Textbooks in Progress \| \| BCcampus \| Current Projects \| \| eCampusOntario \| Open Textbook Initiative \| \| Rebus Community \| Open Textbooks in Development \|	pressbooks	2025-03-22T05:08:52.000976	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/already-available/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/find-open-textbook/	How to Adapt an Open Textbook 7 Find an Open Textbook Typically, most instructors begin their quest for an open textbook by searching the many established open textbook and OER collections available which are listed below. For more information, see Other Open Textbook Collections. Other methods to find open textbooks or other OER: - Connect with your library - Ask your colleagues what OER they use - Conduct an advanced Google search: https://www.google.ca/advanced_search - Get your students to find open resources, have them do a content review, and post the results to your course website or Learning Management System (LMS) Also see, Evaluate an Open Textbook.	pressbooks	2025-03-22T05:08:52.010112	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/find-open-textbook/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/evaluate-open-textbook/	How to Adapt an Open Textbook 8 Evaluate an Open Textbook Open Oregon Educational Resources has developed three checklists–each designed for a specific user type–to evaluate individual course materials such as open textbooks. - Faculty Checklist for Evaluating Course Materials - Administrator Checklist for Evaluating Course Materials - Student Checklist for Evaluating Course Materials Evaluate OER collections British Columbia Open Education Librarians (BCOEL) have developed a useful guide to assist faculty, librarians, educational technologists, and students or anyone else to evaluate the quality of OER collections. By evaluating the following criteria, you can assess an open educational resource repository to identify opportunities to improve the open resources available to your institution: - Authority - Audience - Access and Diversity - User-friendliness - Subject Coverage - Search Functional and Browsing - Media Type - Licensing and Permissions Assess an OER collection with the Open Education Resource Repository Rubric with the open textbook and OER evaluation process.	pressbooks	2025-03-22T05:08:52.020318	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/evaluate-open-textbook/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/plan/	How to Adapt an Open Textbook 9 Make a Plan Before adapting an existing book, it’s important to establish a road map that will guide the timeline of the work, layout and style of the work, and desired changes. Whether your adaptation is small or large, this step is important to ensure a cohesive and consistent final product. Below are tips to help you with style and consistency. Style To help you set this up, see the Style Guide in the BCcampus Open Education Self-Publishing Guide. Consider creating a style sheet as well that identifies the idiosyncrasies of your adaptation in terms of style, such as citation, spellings, and layout. For an adaptation, it is suggested that you follow the citation style used by the original author to maintain consistency throughout the open textbook. Consistency One of the challenges of adapting an open textbook is to create a final product that is consistent throughout. It is highly recommended that you assess the original textbook before you begin. Once this has been done, attempt to match all revised and new text, resources, layout and citation styles to that of the original work. Assess language and tone Begin by assessing the style and tone of the original text. Here are some elements to be aware of: - Is the tone of the language formal, or friendly and conversational? - How does the author address the reader? From a distance? Or does the author include the reader with phrases such as “we learn” and “you will see”? - How is punctuation used? For example, are serial commas used, i.e. a comma before “and” when listing three or more things: “the cat, the dog, and the horse” OR “the cat, the dog and the horse”. - How long is the typical sentence? Paragraph? - Pay attention to the word count for existing chapters (average and range). Try to maintain this count for both new and revised chapters. Ask your project manager for assistance, if required. What is the layout? As you review the textbook, take note of the following: - Does each chapter contain specific pedagogical features such as Learning Objectives, Exercises, Summary, Suggested Readings, highlighted points of interest? - Does the author use lists? If so, are bullets or numbers used or something else? - How are headings used? Are sub-headings used? What is the highest heading level used? - How long are sections under a heading or sub-heading? How are resources used? Resources refer to all items other than text, such as photos, graphs, diagrams and multimedia content (video or audio links). Pay attention to what types of resources the original author used, how often they are inserted and how they are labelled. Ensure all external resources are either released with an open copyright licence or are in the public domain. See Fair Dealing and Fair Use in the Self-Publishing Guide. - Resources should have a caption (e.g., Figure 1 + description). See the Resources: Captions and Attributions section in the Self-Publishing Guide for details. - Differentiate between figures and tables (e.g., Figure 1.2 or Table 1.2). - For adaptations, use the numbering system employed by the original author. - For new creations, use a numbering system that incorporates the chapter number and image sequence. For example, for the first figure in Chapter 1 caption the figure, Figure 1.1. - New types of resources can be added to the adapted version however, keep the overall textbook in mind. When adding a new type of resource ensure that it enhances the flow of the book. - In addition to the above, we suggest the attribution be based on the best practices for attribution recommended by Creative Commons. References and citation style When you assess the textbook, identify both the citation style, and how and where references are listed in the book (e.g., at the end of each chapter, at the end of the book, or as footnotes). Note how in-text citations are used including punctuation. Consider using the same citation style.	pressbooks	2025-03-22T05:08:52.032412	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/plan/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/changes/	How to Adapt an Open Textbook 10 What Are the Changes What will you change? Adapting or changing an existing open textbook doesn’t need to be onerous. The changes you make can be simple such as: - Changing the title of the book, some or all of its chapters or chapter sections - Adding one or two new images - Removing a chapter that isn’t pertinent to your course - Removing a chapter to be used, leaving the rest of the book behind Sometimes, an adaptation might require more than a few simple changes. For example: - A significant number of chapters might be removed, leaving behind just the ones that fit the course curriculum. - Chapters might be reordered to more accurately fit the order in which material is presented in a course. It might be necessary to add material from other open textbooks or open educational resources to the open textbook you are adapting. For more information on where to find openly licensed images and other content, see Resources: Search and Find in the Self-Publishing Guide. Also take a look at Resources: Only the Open. Maybe you decide to write new material to fill in the gaps of an existing textbook such as new examples or exercises. (If you do this and plan to release the finished work as an open textbook, remember that your new work will be included under this licence.) Will it be difficult? How easy or difficult this will be depends on a number of factors, including; - How much content do you wish to change? Do you want to remove chapters, or rewrite entire chapters of content? - What technical format is the original textbook in? A Word document is much easier to modify than a PDF document. - What type of licence is the content released under? Does it have a Creative Commons licence that allows for modification or adaptation of the content? - How comfortable are you with using technology and creating content? Keep a record of changes and additions As the author, you retain copyright of all new material you create. This means that even if the new material you create is released under an open licence, as the author, you will receive attribution for your contribution. As you edit and make changes (text and images) and/or add new material, such as a chapter or section within a chapter, keep a list so these additions/changes: - Can be included as part of the Copyright Notice - Can be accurately attributed to you, the author Minor changes, such as fixing grammatical or spelling mistakes, don’t need to be documented. If you add material from another openly licensed work to your adaptation, especially text, record the source and where it is used in your adapted version. This information is needed for the wording and placement of each attribution statement required for each open CC BY (Creative Commons Attribution) licensed work you use. For more information, see Attribution Statement. Changing images: add new ones or remove old ones With an openly licensed resource, you are welcome to remove images that don’t fit your needs or you can add new ones. You are also permitted to edit existing images. (Check the licence of the image you plan to change to ensure that its permissions fit your intended change.) For more information on: - How to add or edit an image in Pressbooks, see Images and Files in the Pressbooks Guide. - How to caption and attribute openly licensed images, see Resources: Captions and Attributions in the Self-Publishing Guide. - Where to find openly licensed images and other content, see Resources: Search and Find in the Self-Publishing Guide. Use a copy editor and subject-matter expert Even the best author benefits from the keen eyes of a copy editor. This individual looks at your work with fresh eyes and can provide feedback on grammar, spelling, readability, clarity, and consistency. A subject matter expert (SME) — presumably a colleague or other individual who is an expert on the topic you’re writing about — can provide suggestions about the content. It is best that the SME reviews your work before the copy editor. One final step is to have a copy editor (preferably different than the one who copy edits your work) proof read the final draft.	pressbooks	2025-03-22T05:08:52.045659	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/changes/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/attribution-statement/	How to Adapt an Open Textbook 11 Attribution Statements All Creative Commons licences contain an attribution (BY) clause. This means that you must include a statement that gives credit to, or attributes, the creator of the work from which you have borrowed, whether it’s text, an image, a video, or other item. If you have made a change, indicate that in your attribution statement. As far as how and where to place attribution statements for text or media taken from another source or sources, best practices state that you should place them at the bottom of each chapter for which borrowed text or media have been used. Clearly mark all of these with a heading called: “Attributions.” Several attribution statements can be listed under this heading. Attribution Statement examples for text Example of an attribution statement for adapted text (changes made to borrowed text) This chapter is an adaptation of Physical Geography and Natural Disasters by R. Adam Dastrup and is used under a CC BY-NC-SA 4.0 licence. Changes include rewriting some of the passages and adding original material. Note: - The statement is clear, simple, and contains all elements required for a complete attribution: title of the work used, author, and licence type. A link is provided to the original work and the licence type. - There is no need to name the adapting author in this statement. This information should either be included on the Book Info page as a main author OR if you want to indicate which author wrote/adapted which chapter, you can use the Chapter Author option at the bottom of each chapter/web page in Pressbooks. - If the book is to be CC BY licensed, then you will need to address the conditions dictated by the licence for each resource you are using in your adaptation. At the bottom of the Chapter page in Pressbooks, there is an option to set the licence type for that page which will override, for this page only, the overarching licence chosen for the book on the Book Info page. - If the chapter contains some original material AND some material from another source, then rather than saying “This chapter is an adaptation of …”, say “This chapter contains material taken from …” If the chapter only contains material from another source and the material has not been changed (i.e., not adapted), then use an attribution statement like the below example. Example of an attribution statement for borrowed text (no changes made) This chapter is composed of text taken from Chapter 2.2 of Physical Geography and Natural Disasters by R. Adam Dastrup and is used under a CC BY-NC-SA 4.0 licence. No changes have been made nor original material added. Attribution Statement examples for images Example of an attribution statement for an adapted image Figure 1.2 Dog by David Locke is used under a CC BY 2.0 licence. Modifications to this photo include cropping. Note: - The statement is clear, simple and contains all elements required for a complete attribution: title of the image, photographer, and licence type, as well as a note of the changes made. A link is provided to the original work, the home page of the photographer (if available), and the licence type. Example of an attribution statement for a borrowed image (no changes made) Figure 1.2 Dog by David Locke is used under a CC BY 2.0 licence. When copyright is not owned by the author An attribution statement should give credit to the author of a work. However, there are situations when an author does now own the copyright to the work they have created. One exception to copyright ownership involves employment. In these and other cases where authorship and copyright are different, the attribution statement should include a copyright statement. Example of an attribution statement for adapted text where copyright is not owned by the author This chapter is an adaptation of Clinical Procedures for Safer Patient Care by Glynda Rees Doyle and Jodie Anita McCutcheon, © 2015 British Columbia Institute of Technology, and is used under a CC BY 4.0 licence. and Citation vs. Attribution in the Self-Publishing Guide.	pressbooks	2025-03-22T05:08:52.060495	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/attribution-statement/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/choose-licence/	Final Steps 12 Choose a Licence If you are adapting an existing open textbook, the adaptations you make will be released with whatever open licence you choose, while the rest of the book will be released under the licence of the original book. In other words, you need to respect the licence of the original work. You cannot license what you do not create. You can only attach a CC BY (Creative Commons Attribution) or other open licence to the parts of the book that you have created and are new. However, there is a caveat. If the textbook you are adapting has a ShareAlike condition (e.g., CC BY-SA 4.0) stipulated, then you must release the entire book using the same licence as the original book. Attribution: CC BY This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials. Attribution-Share Alike: CC BY-SA This licence lets others remix, tweak, and build upon your work even for commercial purposes, as long as they credit you and license their new creations under the identical terms. This license is often compared to “copyleft” free and open source software licenses. All new works based on yours will carry the same license, so any derivatives will also allow commercial use. This is the license used by Wikipedia, and is recommended for materials that would benefit from incorporating content from Wikipedia and similarly licensed projects. Attribution-No Derivatives: CC BY-ND This licence allows for redistribution, commercial and non-commercial, as long as it is passed along unchanged and in whole, with credit to you. Attribution-Non Commercial: CC BY-NC This licence lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms. Attribution-Non Commercial-Share Alike: CC BY-NC-SA This licence lets others remix, tweak, and build upon your work non-commercially, as long as they credit you and license their new creations under the identical terms. Attribution-Non Commercial-No Derivatives: CC BY-NC-ND This license is the most restrictive of our six main licenses, only allowing others to download your works and share them with others as long as they credit you, but they can’t change them in any way or use them commercially. For more information on Creative Commons licences, see Choose a License. Attributions The Creative Commons license definitions listed at the bottom of this page have been copied from Creative Commons and are used under a CC BY 4.0 license.	pressbooks	2025-03-22T05:08:52.071328	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/choose-licence/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/adaptation-statement/	Final Steps 13 Adaptation Statement If modifying an open textbook that is in the public domain, you are not required to attribute the original author. However, if you are modifying an open textbook that is licensed under one of the Creative Commons licences, then acknowledging the author(s) of the original work is required as well as indicating who owns the copyright, how the original work was licensed, and what the substantive changes were made. There is one caveat. If the textbook you are adapting has a ShareAlike attribute, then you must release the book with the same licence that it was originally released with. The ShareAlike clause means that you must use the exact same licence that was used in the original for ANY adaptation. If using Pressbooks to create an adaptation, the licence type chosen for the adapted portions of your open textbook and the licence used for the original book, should be noted in a couple of places in the book, along with the changes made. You should also indicate who holds the copyright to specific parts of the book. The adaptation statement should be found: - In the preface or introduction in the front matter where it is clearly stated that the book is an adaptation of another open textbook. - As part of the copyright page so readers know which book sections are newly created or changed. If you are creating an adaptation using another system, such as Word, then displaying the adaptation and licensing information on the copyright page of the book is sufficient. Keep in mind that the reader could be viewing a printed copy with page numbers or an electronic version with no page numbers, so do not use page numbers as references. Instead, use chapters and chapter sections as reference points within the book. How detailed should the adaptation statement be As suggested earlier in this guide, it is advisable to keep a detailed record of the changes and additions made to existing material during the revision process. This information is for the adapting author’s reference and can be used to compose a comprehensive adaptation statement. BCcampus Open Education has managed several major adaptations of open textbooks. Based on that experience, these best practices for creating effective and informative adaptation statements. - If a significant amount of new information has been added, briefly describe the content and where it can be found, e.g., as a new chapter or part of an existing one. Provide a title to the new work. - If significant changes have been made to existing material, describe what has been altered and where it can be found, by chapter or chapter section. - Overall updates and changes can be described with a general statement. For examples, see the metadata at the bottom of the home page for these textbooks: - Introduction to Psychology – 1st Canadian Edition - Principles of Social Psychology – 1st International H5P Edition Here is an example of an adaptation statement used on an About the Book page in the front matter of an adapted open textbook created in Pressbooks. The following is an example of the copyright statement. In Pressbooks, this information is added to the Copyright Notice field on the Book Info page. For an adaptation created using another system, such as Word, this information can be added to the preface or other front matter page of the book. Notice that these items are addressed: - The licence under which the book, as a whole, is released. The licence type is hyperlinked to a page on the Creative Commons website that explains this licence. - A short description about the permissions granted by this licence. - Who owns the copyright, when the copyright began, and for which parts of the book. Unless otherwise noted, Introductory Business Statistics with Interactive Spreadsheets – 1st Canadian Edition is (c) 2010 by Thomas K. Tiemann. The textbook content was produced by Thomas K. Tiemann and is licensed under a Creative Commons Attribution 3.0 Unported licence, except for the following changes and additions, which are (c) 2015 by Mohammad Mahbobi, and are licensed under a Creative Commons Attribution 4.0 International licence. All examples have been changed to Canadian references, and information throughout the book, as applicable, has been revised to reflect Canadian content. One or more interactive Excel spreadsheets have been added to each of the eight chapters in this textbook as instructional tools. The following additions have been made to these chapters: Chapter 4 - chi-square test and categorical variables - null and alternative hypotheses for test of independence Chapter 8 - simple linear regression model - least squares method - coefficient of determination - confidence interval for the average of the dependent variable - prediction interval for a specific value of the dependent variable You are free to use or modify (adapt) any of this material providing the terms of the Creative Commons licences are adhered to.	pressbooks	2025-03-22T05:08:52.085562	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/adaptation-statement/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/publish-distribute/	Final Steps 14 Publish and Distribute Once you’ve completed your adaptation, you will need to decide who to share your work with — your students, your colleagues, the bookstore and library at your institution, the open community — and how to do this. Also, what file types will you provide, what ancillary resources will you/could you include, and how will you ensure longevity for your work. File types Students like flexibility when it comes to their textbooks. Some may prefer printed versions of the textbook, others will prefer using a website. Still others will like to use an e-reader or e-reading software. To make your book as accessible as possible, consider making your textbook available in multiple formats so students have the ability to choose the format that works for them. Also remember to include editable files so that others can use your work to create their own adaptations. Pressbooks allows a variety of files to be export from your book, both editable (.xml, .odt, .html, .epub) and less editable (.pdf) files. Share For students, you can distribute your adapted textbook by: - Providing them with a link to the online version - Downloading copies of the book and placing them on another website (e.g., an LMS, Dropbox, or Google Documents), and share that link - Placing copies of the textbooks file on your faculty website and sharing the URL with your students so they can download a copy - Connecting with your bookstore and asking for print copies to be made available for your students For colleagues: - Place copies of the textbooks file, including a link, on your faculty website - If your library agrees to, include your adapted open textbook in your library collection (print copies and/or an online version) and share this with your colleagues For your institution’s bookstore: - Provide them with a link to the online version - Place copies of the textbooks file, including a link, on your faculty website - Ask if they will provide print copies for your students - If your bookstore has a print-on-demand service, ask if they can set this up for your book For your institution’s library: - Ask if they will add your adapted textbook to the library’s collection For your teaching and learning centre: - Ask if they would like a copy - Ask if they have any suggestions on how to distribute your adapted textbook Consider sharing your work with the larger open community. One way to do this is by adding your adapted textbook to an established repository or open textbook collection. Some of the following repositories require a resource to undergo a formal review before being accepted: Ancillary resources Consider providing ancillary resources for your adapted open textbook or asking colleagues with whom you share your adaptation, to share back any supplemental materials they develop for the book. These might include: - PowerPoint slides - Test bank questions and answers - Images - Videos - Student exercises The future of your adaptation Now that you’ve completed your adapted open textbook — whether the changes were minor or major — here are some question you might ask yourself: - How will I track who’s using my book? - How am I going to keep my textbook current? - Do I want feedback on my work? How will I collect this?	pressbooks	2025-03-22T05:08:52.099059	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/publish-distribute/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/adapt-lms-content/	Courses in an LMS 15 Adapt Content in an LMS Adaptations need not be restricted to open textbooks. Many instructors prefer to add openly licensed content from an open textbook to an online course within a learning management system (LMS) such as Moodle or D2L. This is legally possible because Creative Commons’ open-copyright licences do not restrict where content is used, added, or changed as long as the terms of the content’s licence are followed. Like any adapted work, the course within an LMS that contains some or all of an open textbook–or other OER–must include both an attribution and a copyright statement, as well as, lay out the changes and additions made to the original version. Placement of these statements should be at the beginning of the course where it is clear and can be easily found. (See Adaptation Statement.)	pressbooks	2025-03-22T05:08:52.108330	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/adapt-lms-content/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/cite-adaptation-in-lms/	Courses in an LMS 16 How to Cite an Adaptation in an LMS Some instructors wonder how they should cite an open textbook that has been added to and adapted for a LMS. Here are some steps to consider. - - Assign a different title to the LMS version in order to differentiate it from the original open textbook using, for instance, names of the post-secondary institution and course. These details will not only makes citation easier, but identification of the adapted LMS version clearer. For example, if the original title is “Introduction to English” the LMS version for ABC Community College could read: “ABC Community College’s Introduction to English for LMS.” - There should be both an attribution and copyright statement included in the citation that clearly identifies that the LMS version is an adaptation and lays out the changes and additions made. (See Adaptation Statement.) - Authors who have contributed to the adaptation should be included in the citation. Introductory Chemistry and Introductory Chemistry-1st Canadian Edition serve as examples that show how authors are listed in an original open textbook versus its adapted version. For related information see How do I cite content from my online courses?.	pressbooks	2025-03-22T05:08:52.118009	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/cite-adaptation-in-lms/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/adapt-restricted-with-permission/	Challenges 17 How to Adapt a Restricted Textbook with Permission Some times, authors receive permission from a publisher to create a new edition/revision of a textbook or other work for which copyright is owned by the publisher and was previously released or published without the benefit of an open copyright (Creative Commons) licence. This is wonderful news. But how should one proceed? Here are some suggestions on steps to take to ensure that all is legal and correct. Step 1: Double check copyright Confirm that copyright is held by the publisher. If a book is available online, it might be possible to find it there and take a look at the copyright notice which typically follows or (in a printed copy) is on the back of the Title page. Publishers in this situation are typically the copyright holder for the entire book including all chapters, illustrations, images, etc. Some publishers might prefer that the book be released using a CC BY-NC licence so that they can retain the rights to sell physical copies of the book. Step 2: Contributing authors and courtesy When a publisher is the copyright holder for an entire book, there is no requirement to contact the contributing authors. However, you might consider doing so as a professional courtesy. It’s possible that some of these authors would like to see the new edition, and may even be interested in contributing to it. Step 3: Confirm the agreement in writing There are two distinct items that will be openly licensed in this situation. One is the original book. The other are the adaptations and additions made by Jane Plain and John Smith, the adapting authors. Because the publisher is the copyright holder, they are the only ones who can release the original book under a CC BY or open copyright licence. To be clear and certain that you have permission to use and release this previously restrictively licensed work with an open copyright licence, ask the publisher to confirm this agreement in writing. Step 4: Write the adaptation statement The new edition or revision is basically an adaptation. As such, be sure to include language used for an adaptation to the copyright notice. (See Adaptation Statement for more information.) However, the difference in this situation is that the original book was not published with an open copyright licence. Below is an example of how the adaptation statement might read. A list of changes and additions follows this statement. Step 5: Share the adaptation statement In advance of beginning your adaptation project, it is recommended that you spell out what the attribution statement for both the original content and the revision additions will say, and then share the attribution statement with the publisher (and maybe the contributing authors) to ensure that everyone is receiving appropriate credit.	pressbooks	2025-03-22T05:08:52.128263	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/adapt-restricted-with-permission/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://opentextbc.ca/adaptopentextbook/chapter/different-copyright/	Primary Navigation Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices. Book Contents Navigation About BCcampus Open Education Introduction 1. Definition of Adapt 2. Reasons to Adapt an Open Textbook 3. Permission to Adapt 4. Three Steps Before You Begin 5. Start Small 6. Is This Book Already Available? 7. Find an Open Textbook 8. Evaluate an Open Textbook 9. Make a Plan 10. What Are the Changes 11. Attribution Statements 12. Choose a Licence 13. Adaptation Statement 14. Publish and Distribute 15. Adapt Content in an LMS 16. How to Cite an Adaptation in an LMS 17. How to Adapt a Restricted Textbook with Permission 18. How to Attribute When Author Doesn't Own Copyright Appendix 1: How to Canadianize an OER Versioning History Challenges Previous/next navigation Adaptation Guide Copyright © 2016 by BCcampus is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted.	pressbooks	2025-03-22T05:08:52.369093	09-6-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://opentextbc.ca/adaptopentextbook/chapter/different-copyright/", "book_url": "https://opentextbc.ca/adaptopentextbook/front-matter/about-bccampusoe/", "title": "Adaptation Guide", "author": "Lauri M. Aesoph", "institution": "", "subject": "Publishing and book trade, , , ," }
https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/arctic-plants/	Arctic Plants Learning Goals In this chapter, you will learn to: - Identify rhyming words - Copy short sentences - Read common sight words in a paragraph - Answer questions about a story - Give sentence answers to questions - Give an opinion on a reading Talk About It - Would you ever want to go to the arctic? Why or why not? Picture Dictionary \| \|\| Use the pictures to help you figure out the words. \| \|\| \| arctic \| beautiful \| winter \| \| dark \| summer \| ice \| \| water \| heat \| together \| \| wind \| willow \| poppy \| \| bright \| Practice reading these words without the pictures. \| \|\|\|\| \| beautiful \| dark \| winter \| wind \| willow \| \| bright \| heat \| together \| water \| summer \| \| arctic \| ice \| poppy \| Word Skills Word Patterns Two words rhyme when the endings sound the same. For example, these words rhyme: - car and bar - net and jet - fill and hill - pot and rot - nut and cut Find the words that rhyme. The first one is done for you. \| land \| try \| mice \| \|\| \| lot \| ice \| hand \| sky \| dot \| 1. sun run 2. ______________ ______________ 3. ______________ ______________ 4. ______________ ______________ 5. ______________ ______________ Read the sentences. Then copy them. Use upper case letters, periods, and question marks correctly. \| \| The mice hid in the hole. \| \| Do you like this dish? \| \| Do not slip on the ice. \| \| Can we chat? \| \| We had a lot of fun. \| Use Your Reading Skills Listen to Arctic Plants. Then read Arctic Plants in BC Reads: Adult Literacy Fundamental English – Reader 1. Check Your Understanding Answer these questions. Use the underlined words to form a sentence. 1. How long does summer in the arctic last? ___________________________________________________ 2. What plant adds a bit of heat to the ground so other plants can grow? ___________________________________________________ 3. What do the plants do to last in the cold wind? ___________________________________________________ 4. Why is the arctic willow different from other trees? ___________________________________________________ 5. What does the arctic poppy always face? ___________________________________________________ 6. What plant do you like best? ___________________________________________________ Writing Task Think of a time when you had to be strong and brave. Tell the story to your instructor. Your instructor will write down your words. Then copy your story into your notebook. When you are done, read your story. - Did you begin each sentence with an upper case letter? - Did you use an upper case letter at the beginning of a person’s name? - Did you end each sentence with a period or question mark? - Are you missing any words? Answer Key \| \| \| Word Skills \| \| \| QUESTION \| ANSWER \| \| 1 \| sun, run \| \| 2 \| land, hand \| \| 3 \| try, sky \| \| 4 \| mice, ice \| \| 5 \| lot, dot \| \| Check Your Understanding \| \| \| QUESTION \| ANSWER \| \| 1 \| Summer in the arctic lasts for just a few weeks. \| \| 2 \| Arctic moss adds a bit of heat to the ground so other plants can grow. \| \| 3 \| Plants stay together to last in the cold wind. \| \| 4 \| The arctic willow is different from other trees because it grows on its side. \| \| 5 \| The arctic poppy always faces the sun. \| \| 6 \| Answers will vary. \| Attributions See the Attributions page near the end of this book.	pressbooks	2025-03-22T05:08:52.400677	09-16-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/arctic-plants/", "book_url": "https://ecampusontario.pressbooks.pub/adultlitfund1/front-matter/about-bccampus/", "title": "Adult Literacy Fundamental English - Course Pack 1", "author": "Shantel Ivits", "institution": "", "subject": "Language learning: reading skills" }
https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/canadas-tallest-tree/	Canada’s Tallest Tree Learning Goals In this chapter, you will learn to: - Hear, read, and write words with long vowels - Copy short sentences - Read common sight words in a paragraph - Answer questions about a story - Give sentence answers to questions Talk About It - Have you ever gone for a hike? Where? - Do you have a favourite forest to walk in? Picture Dictionary \| \|\| Use the pictures to help you figure out the words. \| \|\| \| hunt \| Vancouver Island \| logger \| \| path \| forest \| tall \| \| awe \| park \| risk \| \| down \| Practice reading these words without the pictures. \| \|\|\|\| \| forest \| awe \| down \| hunt \| tall \| \| risk \| Vancouver Island \| logger \| park \| path \| Word Skills Word Patterns Many words end in e. The e is silent but can make the first vowel in the word say its name. \| cap \| cape \| \| kit \| kite \| \| rob \| robe \| Match the word to the picture. \| rake \| dime \| nose \| cake \| \| nine \| home \| rice \| gate \| \| 1. _____________ \| 2. _____________ \| 3. _____________ \| \| 4. _____________ \| 5. _____________ \| 6. _____________ \| \| 7. _____________ \| 8. _____________ \| You will see these words in the story. Practice reading them. \| name \| like \| made \| \| take \| more \| save \| Read the sentences. Then copy them. Use upper case letters, periods, question marks, and exclamation marks correctly. \| \| Meg has nine cats. \| \| Lock the gate. \| \| What is your name? \| \| Save me! \| \| Can I have more cake? \| Use Your Reading Skills Listen to Canada’s Tallest Tree. Then read Canada’s Tallest Tree in BC Reads: Adult Literacy Fundamental English – Reader 1. Check Your Understanding Answer these questions. Use the underlined words to help you form a sentence. 1. Where is Canada’s tallest tree? ___________________________________________________ 2. How tall is Canada’s tallest tree? ___________________________________________________ 3. Who was the first person to find Canada’s tallest tree? ___________________________________________________ 4. What did the forest with Canada’s tallest tree become? ___________________________________________________ 5. What are many of BC’s old forests at risk of? ___________________________________________________ Writing Task Think of a time something in nature filled you with awe, or made you feel amazed. Tell the story to your instructor. Your instructor will write down your words. Then copy your story into your notebook. When you are done, read your story. - Did you begin each sentence with an upper case letter? - Did you use an upper case letter at the beginning of a person’s name? - Did you end each sentence with a period or question mark? - Are you missing any words? Answer Key \| \| \| Word Skills \| \| \| QUESTION \| ANSWER \| \| 1 \| rice \| \| 2 \| dime \| \| 3 \| gate \| \| 4 \| home \| \| 5 \| rake \| \| 6 \| nose \| \| 7 \| nine \| \| 8 \| cake \| \| Check Your Understanding \| \| \| QUESTION \| ANSWER \| \| 1 \| Canada’s tallest tree is on Vancouver Island. \| \| 2 \| Canada’s tallest tree is 314 feet tall. \| \| 3 \| A logger was the first person to find Canada’s tallest tree. \| \| 4 \| The forest with Canada’s tallest tree is now a park. \| \| 5 \| Many of BC’s old forests are at risk of being cut down. \| Attributions See the Attributions page near the end of this book.	pressbooks	2025-03-22T05:08:52.447004	09-16-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/canadas-tallest-tree/", "book_url": "https://ecampusontario.pressbooks.pub/adultlitfund1/front-matter/about-bccampus/", "title": "Adult Literacy Fundamental English - Course Pack 1", "author": "Shantel Ivits", "institution": "", "subject": "Language learning: reading skills" }
https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/how-to-grow-tomatoes/	Grow Your Own Tomatoes Learning Goals In this chapter, you will learn to: - Hear, read, and write words with short vowels - Copy short sentences - Read common sight words in a paragraph - Answer questions about a story - State an opinion about a reading - Complete sentences by adding a word Talk About It - Do you have any plants? What kind? - What is good about growing your own food? Picture Dictionary Use the pictures to help you figure out the words. \| potting soil \| warm \| window \| \| hour \| tie \| stick \| \| colour \| bottom \| Practice reading these words without the pictures. \| hour \| stick \| bottom \| colour \| \| window \| warm \| potting soil \| tie \| Word Skills Word Patterns You have learned to read these vowel sounds: a says /a/ like apple e says /e/ like Expo i says /i/ like itch o says /o/ like octopus u says /u/ like up You will see these short vowel words in the story. Read each word. Listen to the short vowel sound. Sort it in the list below. \| pot \| big \| sun \| wet \| bit \| \| yet \| top \| add \| pack \| pick \| \| /a/ like apple \| /e/ like Expo \| /i/ like itch \| /o/ like octopus \| /u/ like up \| \| \| \| Can you think of more short vowel words? Add your words to the chart. Read the sentences. Then copy them. Use upper case letters, periods, and question marks correctly. \| \| Is the pot hot? \| \| Did you pack yet? \| \| The bug bit me. \| \| Do not pop the zit. \| \| We sat in the sun. \| Use Your Reading Skills Listen to Grow Your Own Tomatoes. Then read Grow Your Own Tomatoes in BC Reads: Adult Literacy Fundamental English – Reader 1. Check Your Understanding \| sun \| ripe \| pot \| seed \| wet \| - Fill the _____________ with soil. - Put a _____________ in the soil. - Add water. Keep the soil _____________. - Put the pot by a window with lots of _____________. - Pick the tomatoes when they are _____________. Writing Task Have you ever picked fruit, such as berries, in a field, forest, or orchard? Tell the story to your instructor. Your instructor will write down your words. Then copy your story into your notebook. When you are done, read your story. - Did you begin each sentence with an upper case letter? - Did you use an upper case letter at the beginning of a person’s name? - Did you end each sentence with a period or question mark? - Are you missing any words? Answer Key \| \|\|\|\|\|\|\|\|\|\|\| \| Word Skills \| \|\|\|\|\|\|\|\|\|\|\| \| \|\|\|\|\|\|\|\|\|\|\| \| Check Your Understanding \| \|\|\|\|\|\|\|\|\|\|\| \| QUESTION \| ANSWER \| \|\|\|\|\|\|\|\|\|\| \| 1 \| pot \| \|\|\|\|\|\|\|\|\|\| \| 2 \| seed \| \|\|\|\|\|\|\|\|\|\| \| 3 \| wet \| \|\|\|\|\|\|\|\|\|\| \| 4 \| sun \| \|\|\|\|\|\|\|\|\|\| \| 5 \| ripe \| Attributions See the Attributions page near the end of this book.	pressbooks	2025-03-22T05:08:52.476829	09-16-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/how-to-grow-tomatoes/", "book_url": "https://ecampusontario.pressbooks.pub/adultlitfund1/front-matter/about-bccampus/", "title": "Adult Literacy Fundamental English - Course Pack 1", "author": "Shantel Ivits", "institution": "", "subject": "Language learning: reading skills" }
https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/tomatoes/	Tomatoes Learning Goals In this chapter, you will learn to: - Hear, read, and write words with digraphs (sh, th, ch, and ck) - Copy short sentences - Read common sight words in a paragraph - Answer questions about a story - State an opinion about a reading - Name some common symbols Talk About It - What foods can be made with tomatoes? - What is your favourite food that can be made with tomatoes? Picture Dictionary \| \|\| Use the pictures to help you figure out the words. \| \|\| \| South America \| Europe \| doctor \| \| world \| sauce \| soup \| \| juice \| salsa \| ketchup \| \| leaf \| stem \| space \| \| fight \| Spain \| taste \| Practice reading these words without the pictures. \| \|\|\|\| \| doctor \| sauce \| leaf \| taste \| fight \| \| juice \| soup \| world \| stem \| Europe \| \| South America \| Spain \| space \| ketchup \| salsa \| Word Skills Word Patterns \| A digraph is when two letters together make one sound. Here are some common digraphs. \| \| \| sh like in ship \| \| \| ch like in chin \| \| \| th like in thumb \| \| \| ck like in sock \| Say what is in the picture. What digraph is in each word? \| 1 \| 2 \| 3 \| \| sh ch th ck \| sh ch th ck \| sh ch th ck \| \| 4 \| 5 \| 6 \| \| sh ch th ck \| sh ch th ck \| sh ch th ck \| \| 7 \| 8 \| 9 \| \| sh ch th ck \| sh ch th ck \| sh ch th ck \| How many sounds do you hear in these words? \| \|\| \| 10 \| mop \| 3 \| \| 11 \| sun \| _______ \| \| 12 \| web \| _______ \| \| 13 \| fish \| _______ \| \| 14 \| lock \| _______ \| \| 15 \| crab \| _______ \| \| 16 \| _______ \| Read the sentences. Then copy them. Use upper case letters, periods, question marks, and exclamation marks correctly. \| \| Get in the bath. \| \| The fish is wet. \| \| Do not lock me in! \| \| I cut my chin. \| Use Your Reading Skills Listen to Tomatoes. Then read Tomatoes in BC Reads: Adult Literacy Fundamental English – Reader 1. Check Your Understanding Answer these questions. Use the underlined words from the question to form your answer. 1. Who used to tell people that tomatoes would make them sick? ____________________________________________ 2. What part of the tomato plant will make people sick? ____________________________________________ 3. What place has a big tomato fight every year? ____________________________________________ 4. Why do farms pick tomatoes before they are ripe? ____________________________________________ Writing Grammar Rule This is how to write an address: 639 Sun Street Golden, British Columbia V0A 1H0 Write your address on the lines below. _______________________________________ _______________________________________ _______________________________________ Match the word with the correct symbol. \| equals \| dollars \| and \| \| at \| plus \| & \| $ \| + \| = \| @ \| \| 1. _____ \| 2. _____ \| 3. _____ \| 4. _____ \| 5. _____ \| Fill in the blank with the correct symbol. 6. The bill came to ___ 10.00. 7. My email is tim___gmail.com. 8. Kim ___ Sam got a cat. 9. One ___ one = two. 10. Two + two ___ 4. Writing Task Think of a food you like to make using tomatoes. Tell your instructor how you make this food. Your instructor will write down your words. Then copy your story into your notebook. When you are done, read your story. - Did you begin each sentence with an upper case letter? - Did you use an upper case letter at the beginning of a person’s name? - Did you end each sentence with a period or question mark? - Are you missing any words? Answer Key \| \| \| Word Skills \| \| \| QUESTION \| ANSWER \| \| 1 \| fish, sh \| \| 2 \| rock, ck \| \| 3 \| duck, ck \| \| 4 \| chip, ch \| \| 5 \| bath, th \| \| 6 \| cheese, ch \| \| 7 \| sheep, sh \| \| 8 \| three, th \| \| 9 \| lock, ck \| \| 10 \| 3 \| \| 11 \| 3 \| \| 12 \| 3 \| \| 13 \| 3 \| \| 14 \| 3 \| \| 15 \| 4 \| \| 16 \| 4 \| \| Check Your Understanding \| \| \| QUESTION \| ANSWER \| \| 1 \| Doctors used to tell people that tomatoes would make them sick. \| \| 2 \| A tomato leaf or stem will make people sick. \| \| 3 \| Spain has a big tomato fight every year. \| \| 4 \| Farms pick tomatoes before they are ripe because they last longer this way. \| \| Writing \| \| \| QUESTION \| ANSWER \| \| 1 \| and \| \| 2 \| dollars \| \| 3 \| plus \| \| 4 \| equals \| \| 5 \| at \| \| 6 \| $ \| \| 7 \| @ \| \| 8 \| & \| \| 9 \| + \| \| 10 \| = \| Attributions See the Attributions page near the end of this book.	pressbooks	2025-03-22T05:08:52.519869	09-16-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/tomatoes/", "book_url": "https://ecampusontario.pressbooks.pub/adultlitfund1/front-matter/about-bccampus/", "title": "Adult Literacy Fundamental English - Course Pack 1", "author": "Shantel Ivits", "institution": "", "subject": "Language learning: reading skills" }
https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/gm-food/	GM Food Learning Goals In this chapter, you will learn to: - Hear, read, and write blends with r, l, and t - Copy short sentences - Read common sight words in a paragraph - Answer questions about a story - State an opinion about a reading - Give sentence answers to questions Talk About It - Have you heard that people in labs make new kinds of fruits and vegetables? - Would you mind eating fruits and vegetables that were made by people in a lab? Picture Dictionary \| \|\| Use the pictures to help you figure out the words. \| \|\| \| hard \| cold \| weather \| \| crop \| rain \| fruits \| \| sold \| change \| company \| \| help \| bird \| test \| Practice reading these words without the pictures. \| \|\|\| \| sold \| test \| fruits \| crop \| \| hard \| help \| weather \| rain \| \| bird \| change \| company \| cold \| Word Skills Word Patterns A consonant is any letter that is not a vowel. For example, the letters b, c, d, f, g, h, and j are consonants. A blend is when two consonants go together and each consonant makes a sound. The letters dr in drip are a blend. Read these words. Underline the blend. The first one is done for you. \| \|\| \| 1. clap \| 2. frog \| 3. step \| \| 4. plum \| 5. drum \| 6. stop \| \| 7. club \| 8. crib \| 9. star \| \| 10. flag \| 11. crab \| 12. stem \| Practice reading these words without the pictures. \| \|\| \| crab \| star \| club \| \| stem \| plum \| clap \| \| step \| flag \| crib \| \| frog \| stop \| drum \| Your instructor will read these words to you. Underline the blend in each word. You will see these words in the story. \| \|\| \| crop \| fruit \| grow \| \| dry \| stay \| cold \| \| sold \| test \| Read the sentences. Then copy them. Use upper case letters, periods, and question marks correctly. \| \| Stop the car. \| \| Do not step on the bug. \| \| We put up a flag. \| \| He hit the drum. \| \| Was she bitten by a crab? \| Use Your Reading Skills Listen to GM Food. Then read GM Food in BC Reads: Adult Literacy Fundamental English – Reader 1. Check Your Understanding Does each sentence say something good or bad about GM food? \| 1. GM food can grow in cold weather. \| good \| bad \| \| 2. GM food may kill bugs we need, like butterflies. \| good \| bad \| \| 3. GM food may make birds sick. \| good \| bad \| \| 4. GM food can stop bugs from eating it. \| good \| bad \| \| 5. GM food may make people sick. \| good \| bad \| \| 6. There have not been many tests on GM food. \| good \| bad \| \| 7. GM food may grow in dry land in Africa. \| good \| bad \| \| 8. GM food can stay good longer. \| good \| bad \| 9. Do you think farmers should grow GM food? Use the underlined words to help form your answer. ____________________________________________________ Writing Grammar Rule You can use words from a question to form an answer. Question: Why is farming a hard job? Answer: Farming is a hard job because cold weather can kill your crops. Two things changed from the question to the answer: - is farming changed to farming is - because was used Answer this question in three more ways. Why is farming a hard job? 1. _____________________________________________________ _____________________________________________________ 2. _____________________________________________________ _____________________________________________________ 3. _____________________________________________________ _____________________________________________________ Writing Task Have you ever visited a farm? Or have you ever visited a farmers’ market? Tell your instructor about your visit. Your instructor will write down your words. Then copy your story into your notebook. When you are done, read your story. - Did you begin each sentence with an upper case letter? - Did you use an upper case letter at the beginning of a person’s name? - Did you end each sentence with a period or question mark? - Are you missing any words? Answer Key \| \| \| Word Patterns \| \| \| QUESTION \| ANSWER \| \| 1 \| clap \| \| 2 \| frog \| \| 3 \| step \| \| 4 \| plum \| \| 5 \| drum \| \| 6 \| stop \| \| 7 \| club \| \| 8 \| crib \| \| 9 \| star \| \| 10 \| flag \| \| 11 \| crab \| \| 12 \| stem \| \| Check Your Understanding \| \| \| QUESTION \| ANSWER \| \| 1 \| good \| \| 2 \| bad \| \| 3 \| bad \| \| 4 \| good \| \| 5 \| bad \| \| 6 \| bad \| \| 7 \| good \| \| 8 \| good \| \| Writing \| \| \| (Any three of the sentences below are correct.) \| \| \| Farming is a hard job because bugs can eat your crops. \| \| \| Farming is a hard job because weeds can hurt your crops. \| \| \| Farming is a hard job because your crops may need more rain than they get. \| \| \| Farming is a hard job because fruits and vegetables can go bad before they are sold. \| Attributions See the Attributions page near the end of this book.	pressbooks	2025-03-22T05:08:52.556123	09-16-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/gm-food/", "book_url": "https://ecampusontario.pressbooks.pub/adultlitfund1/front-matter/about-bccampus/", "title": "Adult Literacy Fundamental English - Course Pack 1", "author": "Shantel Ivits", "institution": "", "subject": "Language learning: reading skills" }
https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/vals-garden/	Val’s Garden Learning Goals In this chapter, you will learn to: - Print the alphabet in order using lower case letters - Read and write words with the endings -s, -ing, and -ed - Copy short sentences - Read common sight words in a paragraph - Answer questions about a story - Complete sentences by adding a missing word - Use an upper case letter to begin a person’s name Talk About It - Do you like to get to know the people who live near you? Why or why not? Picture Dictionary \| \|\| Use the pictures to help you figure out the words. \| \|\| \| new \| city \| vegetables \| \| woman \| sidewalk \| carrots \| \| tomatoes \| beans \| peas \| \| talk \| family \| door \| Practice reading these words without the pictures. \| \|\|\| \| sidewalk \| peas \| carrots \| family \| \| door \| city \| woman \| vegetables \| \| talk \| beans \| new \| tomatoes \| Word Skills Word Patterns Words can have endings. Some common word endings are –s, –ing, and –ed. \| –s \| –ing \| –ed \| \| \| look \| looks \| looking \| looked \| \| pull \| pulls \| pulling \| pulled \| \| talk \| talks \| talking \| talked \| 1. Fill in this chart. \| –s \| –ing \| –ed \| \| \| bill \| \|\|\| \| fill \| \|\|\| \| walk \| \|\|\| \| pick \| \|\|\| \| kick \| Word Patterns \| The word ending –s can show there is more than one of something. \| \| \| carrot \| carrots \| \| bean \| beans \| \| pea \| peas \| How many do you see? \| \|\| \| 2. \| egg or eggs? \| \| \| 3. \| rock or rocks? \| \| \| 4. \| bug or bugs? \| \| \| 5. \| pen or pens? \| \| \| 6. \| tree or trees? \| \| \| 7. \| rat or rats? \| Read the sentences. Then copy them. Use upper case letters, periods, and question marks correctly. \| \| The dog naps with the cats. \| \| This log has bugs on it. \| \| Tim jogs a lot. \| \| The kids got on the bus. \| \| Can you get the red cups? \| Use Your Reading Skills Listen to Val’s Garden. Then read Val’s Garden in BC Reads: Adult Literacy Fundamental English – Reader 1. Check Your Understanding \| garden \| family \| new \| vegetables \| \| box \| passes on \| Val \| sidewalk \| Fill in the blanks with the right word or words. 1. I am _______________ to the city. 2. A woman named _______________ lives next door. 3. She grows a vegetable garden by the _______________. 4. She gives me a _______________ of vegetables. 5. She _______________. 6. I take care of her _______________. 7. A _______________ moves next door. 8. I give them a box of _______________ from Val’s garden. Writing Grammar Rule You can use words from a question to make your own sentence. \| Question: \| Who has a garden? \| \| Answer: \| Val has a garden. \| \| Question: \| What vegetables are in Val’s garden? \| \| Answer: \| Carrots, tomatoes, beans, and peas are in Val’s garden. \| \| Question: \| Who moves next door when Val passes on? \| \| Answer: \| A family moves next door when Val passes on. \| \| Question: \| What is given to the family next door? \| \| Answer: \| A box of vegetables is given to the family next door. \| Answer the questions. Use the underlined words to form your answer. 1. Who talks to plants? ____________________________________________________ 2. What gets dry after Val passes on? ____________________________________________________ 3. What looks sad after Val passes on? ____________________________________________________ 4. Write all the letters of the alphabet in order. Use lower case letters. __________________________________________________________________ __________________________________________________________________ __________________________________________________________________ Writing Task Think of a time when a neighbour was nice to you, or a time when you were nice to a neighbour. Tell the story to your instructor. Your instructor will write down what you say. Then copy the story into your notebook. When you are done, read your story. - Did you begin each sentence with an upper case letter? - Did you use an upper case letter at the beginning of a person’s name? - Did you end each sentence with a period or question mark? - Are you missing any words? Answer Key \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| Word Skills \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| QUESTION \| ANSWER \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 1 \| \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 2 \| eggs \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 3 \| rocks \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 4 \| bug \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 5 \| pens \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 6 \| tree \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 7 \| rat \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| Check Your Understanding \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| QUESTION \| ANSWER \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 1 \| new \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 2 \| Val \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 3 \| sidewalk \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 4 \| box \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 5 \| passes on \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 6 \| garden \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 7 \| family \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 8 \| vegetables \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| Writing \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| QUESTION \| ANSWER \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 1 \| Val talks to plants. \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 2 \| The dirt gets dry after Val passes on. \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 3 \| The plants looks sad after Val passes on. \| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\| \| 4 \| a b c d e f g h i j k l m n o p q r s t u v w x y z \| Attributions See the Attributions page near the end of this book.	pressbooks	2025-03-22T05:08:52.599498	09-16-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/vals-garden/", "book_url": "https://ecampusontario.pressbooks.pub/adultlitfund1/front-matter/about-bccampus/", "title": "Adult Literacy Fundamental English - Course Pack 1", "author": "Shantel Ivits", "institution": "", "subject": "Language learning: reading skills" }
https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/the-sack-garden/	The Sack Garden Learning Goals In this chapter, you will learn to: - Print the alphabet in order using upper case letters - Hear, read, and write words with a short /e/ - Hear, read, and write words with a long /e/ (spelled ee) - Copy short sentences - Read common sight words in a paragraph - Answer questions about a story - Describe the sequence of a story - State an opinion about a reading - Use an upper case letter to begin a person’s name Talk About it - Have you ever had a garden? What did you grow? - What did you like about having a garden? - What was hard about having a garden? Picture Dictionary \| \|\| Use the pictures to help you figure out the words. \| \|\| \| South Africa \| three \| white \| \| farm \| money \| land \| \| sack \| rock \| middle \| \| dirt \| one \| hole \| Practice reading these words without the pictures. \| \|\|\| \| one \| sack \| land \| dirt \| \| South Africa \| hole \| money \| three \| \| white \| middle \| farm \| rock \| Word Skills The letters a, e, i, o, and u are called vowels. Vowels have short sounds and long sounds. So far, you have looked at short vowel sounds: - A short /a/ says /a/ like in apple. - A short /i/ says /i/ like in itch. A short /o/ says /o/ like in octopus. - A short /u/ says /u/ like in up. Word Patterns A short /e/ makes an /e/ sound like in Expo. Match each word to a picture. \| bed \| pet \| net \| red \| \| web \| pen \| wet \| vet \| \| 1. ____________ \| 2. ____________ \| 3. ____________ \| \| 4. ____________ \| 5. ____________ \| 6. ____________ \| \| 7. ____________ \| 8. ____________ \| Word Patterns Vowels can also make a long sound. The letters ee make a long /e/ sound like in jeep. Match each word to a picture. \| see \| bee \| green \| weed \| \| tree \| feet \| three \| feed \| \| 9. __________ \| 10. __________ \| 11. __________ \| \| 12. __________ \| 13. __________ \| 14. __________ \| \| 15. __________ \| 16. __________ \| Pick the word that matches the picture. \| \|\|\| \| 17. \| wet \| weet \| \| \| 18. \| neet \| net \| \| \| 19. \| weed \| wed \| \| \| 20. \| se \| see \| \| \| 21. \| web \| weeb \| \| \| 22. \| peet \| pet \| Read these sentences. Then copy them. Use upper case letters correctly. Use periods and question marks correctly. \| \| My pet is at the vet. \| \| I did not see the bee. \| \| I got my feet wet. \| \| This is a red pen. \| \| She is not in bed. \| You will see these words in the reading. They have the short /e/ sound like Expo. \| Meg \| rest \| eggplant \| You will see these words in the reading. They have the long /e/ sound like jeep. \| three \| feed \| weeds \| \| green \| need \| Use Your Reading Skills Listen to The Sack Garden. Then read The Sack Garden in BC Reads: Adult Literacy Fundamental English – Reader 1. Check Your Understanding 1. Where does Meg live? Meg lives in _____________________. 2. Who owns most of the farmland in South Africa? _____________________ own most of the farmland in South Africa. 3. What does Meg use to grow a garden? Meg uses a _____________________ to grow a garden. Are these sentences true? Circle yes or no. \| 4. A sack garden costs a lot of money. \| yes \| no \| \| 5. A sack garden takes up a lot of room. \| yes \| no \| \| 6. A sack garden does not have many weeds. \| yes \| no \| 7. How do you make a sack garden? Put these steps in the right order (1, 2, 3). _____ Make little holes in the side of the sack. _____ Put dirt and rocks in the sack. _____ Put plants in the holes. Writing Write all the letters of the alphabet in order. Use upper case letters. __________________________________________________________________ __________________________________________________________________ __________________________________________________________________ Grammar Rule Use an upper case letter at the beginning of a person’s name. This is Meg. – YES This is meg. – NO Write your full name on the line below. Use upper case letters to begin each name. _______________________________________________________ Find a classmate. Write their full name on the line below. Use upper case letters to begin each name. _______________________________________________________ Find another classmate. Write their full name on the line below. Use upper case letters to begin each name. _______________________________________________________ Writing Task Have you ever had a garden? If so, tell your instructor about your garden. Your instructor will write down your words. Then copy your story into your notebook. If not, imagine you were going to plant a garden. Think about what you would want to grow. Your instructor will write down your words. Then copy your story into your notebook. When you are done, read your story. - Did you begin each sentence with an upper case letter? - Did you use an upper case letter at the beginning of a person’s name? - Did you end each sentence with a period or question mark? - Are you missing any words? Answer Key \| \| \| Word Skills \| \| \| QUESTION \| ANSWER \| \| 1 \| web \| \| 2 \| net \| \| 3 \| red \| \| 4 \| vet \| \| 5 \| pen \| \| 6 \| bed \| \| 7 \| pet \| \| 8 \| wet \| \| 9 \| green \| \| 10 \| bee \| \| 11 \| feet \| \| 12 \| see \| \| 13 \| feed \| \| 14 \| tree \| \| 15 \| three \| \| 16 \| weed \| \| 17 \| wet \| \| 18 \| net \| \| 19 \| weed \| \| 20 \| see \| \| 21 \| web \| \| 22 \| pet \| \| Check Your Understanding \| \| \| QUESTION \| ANSWER \| \| 1 \| South Africa \| \| 2 \| white people \| \| 3 \| sack \| \| 4 \| no \| \| 5 \| no \| \| 6 \| yes \| \| 7 \| 2, 1, 3 \| \| Writing \| \| \| A B C D E F G H I J K L M N O P Q R S T U V W X Y Z \| Attributions See the Attributions page near the end of this book.	pressbooks	2025-03-22T05:08:52.642720	09-16-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/the-sack-garden/", "book_url": "https://ecampusontario.pressbooks.pub/adultlitfund1/front-matter/about-bccampus/", "title": "Adult Literacy Fundamental English - Course Pack 1", "author": "Shantel Ivits", "institution": "", "subject": "Language learning: reading skills" }
https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/chapter-1/	Sweetgrass Learning Goals In this chapter, you will learn to: - Print the alphabet in upper case letters - Hear, read, and write words with a short /a/ and a short /i/ - Copy short sentences - Read common sight words in a paragraph - Answer questions about a story - Complete sentences with original ideas - Begin a sentence with an upper case letter Talk About It - What are some ways that plants help people? Picture Dictionary \| \|\| Use the pictures to help you figure out the words. \| \|\| \| hurt \| street \| braid \| \| house \| grass \| break \| \| feelings \| bend \| walk \| \| roots \| Practice reading the words without the pictures. \| \|\|\|\| \| street \| house \| walk \| bend \| break \| \| hurt \| grass \| roots \| braid \| feelings \| Word Skills Word Patterns The letters a, e, i, o, and u are called vowels. The letter a says /a/ like in apple. Match the word to the picture. \| cap \| sad \| rat \| sat \| \| fat \| map \| mad \| nap \| \| 1. ____________ \| 2. ____________ \| 3. ____________ \| \| 4.____________ \| 5.____________ \| 6. ____________ \| \| 7. ____________ \| 8.____________ \| Word Patterns The letter i says /i/ like in itch. Match the word to the picture. \| lip \| wig \| rip \| \| hit \| fin \| dig \| \| 9. ____________ \| 10. ____________ \| 11. ____________ \| \| 12. ____________ \| 13. ____________ \| 14. ____________ \| Pick the word that matches the picture. \| \|\|\| \| 15. \| rit \| rat \| \| \| 16. \| fan \| fin \| \| \| 17. \| nap \| nip \| \| \| 18. \| wag \| wig \| \| \| 19. \| sad \| sid \| \| \| 20. \| fit \| fat \| Read these sentences. Practice writing them. \| \| The rat had a nap. \| \| The map has a rip. \| \| He has a fat lip. \| \| She sat on the cap. \| \| I am mad. \| You will see these words in the story. They all have the /a/ sound like apple. \| Max \| at \| grass \| \| asks \| after \| that \| \| away \| bad \| happy \| You will see these words in the story. They all have the /i/ sound like itch. \| live \| kids \| in \| \| is \| pick \| it \| \| if \| this \| will \| Use Your Reading Skills Listen to the story Sweetgrass. Then read Sweetgrass in BC Reads: Adult Literacy Fundamental English – Reader 1. Check Your Understanding Circle yes if the sentence is right. Circle no if the sentence is wrong. \| 1. Dan used to live on the street. \| yes \| no \| \| 2. Max and Dan are both Cree. \| yes \| no \| \| 3. Sweetgrass is tall. \| yes \| no \| \| 4. Sweetgrass breaks when you step on it. \| yes \| no \| \| 5. Max and Dan eat the sweetgrass. \| yes \| no \| \| 6. Dan says sweetgrass takes away bad feelings. \| yes \| no \| Writing Below are all the letters of the alphabet. They are in lower case. Write each letter in upper case. The first one is done for you. \| a ___A___ \| b _______ \| c _______ \| d _______ \| \| e _______ \| f _______ \| g _______ \| h _______ \| \| i _______ \| j _______ \| k _______ \| l _______ \| \| m _______ \| n _______ \| o _______ \| p _______ \| \| q _______ \| r _______ \| s _______ \| t _______ \| \| u _______ \| v _______ \| w _______ \| x _______ \| \| y _______ \| z _______ \| Grammar Rule Always use an upper case letter at the beginning of a sentence. She has a red wig. – YES she has a red wig. – NO Writing Task What makes you feel better when you feel bad? Fill in the blanks. Your instructor will help you spell. Example: I feel better when I go for a walk. 1. I feel better when I ________________________________________. 2. I feel better when I ________________________________________. 3. I feel better when I ________________________________________. Answer Key \| \| \| Word Skills \| \| \| QUESTION \| ANSWER \| \| 1 \| map \| \| 2 \| rat \| \| 3 \| nap \| \| 4 \| mad \| \| 5 \| cap \| \| 6 \| sat \| \| 7 \| fat \| \| 8 \| sad \| \| 9 \| dig \| \| 10 \| hit \| \| 11 \| wig \| \| 12 \| rip \| \| 13 \| fin \| \| 14 \| lip \| \| 15 \| rat \| \| 16 \| fin \| \| 17 \| nap \| \| 18 \| wig \| \| 19 \| sad \| \| 20 \| fat \| \| Check Your Understanding \| \| \| QUESTION \| ANSWER \| \| 1 \| no \| \| 2 \| yes \| \| 3 \| yes \| \| 4 \| no \| \| 5 \| no \| \| 6 \| yes \| \| Writing \| \| \| LOWER CASE \| UPPER CASE \| \| a \| A \| \| b \| B \| \| c \| C \| \| d \| D \| \| e \| E \| \| f \| F \| \| g \| G \| \| h \| H \| \| i \| I \| \| j \| J \| \| k \| K \| \| l \| L \| \| m \| M \| \| n \| N \| \| o \| O \| \| p \| P \| \| q \| Q \| \| r \| R \| \| s \| S \| \| t \| T \| \| u \| U \| \| v \| V \| \| w \| W \| \| x \| X \| \| y \| Y \| \| z \| Z \| Attributions See the Attributions page near the end of this book.	pressbooks	2025-03-22T05:08:52.693935	09-16-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/chapter-1/", "book_url": "https://ecampusontario.pressbooks.pub/adultlitfund1/front-matter/about-bccampus/", "title": "Adult Literacy Fundamental English - Course Pack 1", "author": "Shantel Ivits", "institution": "", "subject": "Language learning: reading skills" }
https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/a-new-flower/	A New Flower Learning Goals In this chapter, you will learn to: - Print the alphabet in lower case letters - Hear, read, and write words with a short /o/ and a short /u/ - Copy short sentences - Read common sight words in a paragraph - Answer questions about a story - State an opinion about a reading - End a sentence with a period or question mark Talk About It - Do you have any grandparents? What will you always remember about them? Picture Dictionary \| \|\| Use the pictures to help you figure out the words. \| \|\| \| grandma \| plane \| flowers \| \| garden \| club \| know \| \| long \| time \| home \| \| smile \| Practice reading these words without the pictures. \| \|\|\|\| \| time \| garden \| smile \| home \| club \| \| flowers \| long \| know \| plane \| grandma \| Word Skills Word Patterns The letters a, e, i, o, and u are called vowels. The letter o makes an /o/ sound like in octopus. Match each word to a picture. \| log \| mop \| dot \| jog \| hot \| \| cot \| cop \| pot \| top \| box \| \| 1. ____________ \| 2. ____________ \| 3. ____________ \| \| 4. ____________ \| 5. ____________ \| 6. ____________ \| \| 7. ____________ \| 8. ____________ \| 9. ____________ \| \| 10. ____________ \| Word Patterns The letter u says /u/ like in up. Match each word to a picture. \| sun \| mud \| rug \| mug \| pup \| \| bug \| cup \| gum \| nut \| \| 11. ____________ \| 12. ____________ \| 13. ____________ \| \| 14. ____________ \| 15. ____________ \| 16. ____________ \| \| 17. ____________ \| 18.____________ \| 19. ____________ \| Pick the word that matches the picture. \| \|\|\| \| 20. \| mup \| mop \| \| \| 21. \| bug \| bog \| \| \| 22. \| gum \| gom \| \| \| 23. \| jog \| jug \| \| \| 24. \| cop \| cup \| \| \| 25. \| bux \| box \| Read these sentences. Practice writing the words. Begin with an upper case letter. \| \| The mug is hot. \| \| Is this gum on the rug? \| \| Can you mop up the mud? \| \| The pup had a nap on the cot. \| \| The cop sat on the log. \| You will see these words in the story. They all have the /o/ sound like octopus. \| lot \| not \| on \| You will see these words in the story. They all have the /u/ sound like up. \| clubs \| but \| hunt \| Use Your Reading Skills Listen to A New Flower. Then read A New Flower in BC Reads: Adult Literacy Fundamental English – Reader 1. Check Your Understanding 1. Who goes on a plane for the first time? ________________ goes on a plane for the first time. 2. What does the grandma see that she has never seen before? The grandma sees a ________________. 3. What three things do men do back home? Back home, men ________________, ________________, and ________________. 4. What makes Dave different from the men back home? Dave is different from the men back home because he knows about ________________. 5. Do you think the grandma likes Dave? Why or why not? Discuss with a classmate. Writing Below are all the letters of the alphabet. They are in upper case. Write each letter in lower case. The first one is done for you. \| A ___a___ \| B _______ \| C _______ \| D _______ \| \| E _______ \| F _______ \| G _______ \| H _______ \| \| I _______ \| J _______ \| K _______ \| L _______ \| \| M _______ \| N _______ \| O _______ \| P _______ \| \| Q _______ \| R _______ \| S _______ \| T _______ \| \| U _______ \| V _______ \| W _______ \| X _______ \| \| Y _______ \| Z _______ \| Grammar Rule A sentence can be a question or a statement. A question asks for information. It ends with a question mark. What is your name? – YES What is your name – NO What is your name. – NO A statement gives information. It ends with a period. My name is Ted. – YES My name is Ted – NO Writing Task Think of a time when you acted differently from how people expect a man or woman to act. Tell the story to your instructor. Your instructor will write down your words. Then copy your story into your notebook. When you are done, read your story. - Did you begin each sentence with an upper case letter? - Did you end each sentence with a period or question mark? - Are you missing any words? Answer Key \| \| \| Word Skills \| \| \| QUESTION \| ANSWER \| \| 1 \| cop \| \| 2 \| box \| \| 3 \| mop \| \| 4 \| dot \| \| 5 \| top \| \| 6 \| cot \| \| 7 \| jog \| \| 8 \| pot \| \| 9 \| log \| \| 10 \| hot \| \| 11 \| pup \| \| 12 \| nut \| \| 13 \| bug \| \| 14 \| mud \| \| 15 \| sun \| \| 16 \| gum \| \| 17 \| rug \| \| 18 \| mug \| \| 19 \| cup \| \| 20 \| mop \| \| 21 \| bug \| \| 22 \| gum \| \| 23 \| jog \| \| 24 \| cup \| \| 25 \| box \| \| Check Your Understanding \| \| \| QUESTION \| ANSWER \| \| 1 \| The grandma \| \| 2 \| flower \| \| 3 \| hunt, fish, farm \| \| 4 \| flowers \| \| Writing \| \| \| UPPER CASE \| LOWER CASE \| \| A \| a \| \| B \| b \| \| C \| c \| \| D \| d \| \| E \| e \| \| F \| f \| \| G \| g \| \| H \| h \| \| I \| i \| \| J \| j \| \| K \| k \| \| L \| l \| \| M \| m \| \| N \| n \| \| O \| o \| \| P \| p \| \| Q \| q \| \| R \| r \| \| S \| s \| \| T \| t \| \| U \| u \| \| V \| v \| \| W \| w \| \| X \| x \| \| Y \| y \| \| Z \| z \| Attributions See the Attributions page near the end of this book.	pressbooks	2025-03-22T05:08:52.741425	09-16-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/adultlitfund1/chapter/a-new-flower/", "book_url": "https://ecampusontario.pressbooks.pub/adultlitfund1/front-matter/about-bccampus/", "title": "Adult Literacy Fundamental English - Course Pack 1", "author": "Shantel Ivits", "institution": "", "subject": "Language learning: reading skills" }
https://boisestate.pressbooks.pub/world300template/chapter/intercultural-competency/	Language Proficiency Intercultural Competency (Remove these instructions after reading) Instructions: Replace the placeholder artifacts and the descriptions with your own content. What is Intercultural Competence? “The need for language competence in a global society touches every sector of life. From career preparation in an international workforce to citizen diplomacy and national defense to one’s role in a social or virtual community, communication across cultures is key. Learners today must have the language proficiency to communicate with global audiences, the insight into the cultural perspectives that shape those audiences, and the ability to behave appropriately in a variety of cultural contexts.” – NCSSFL-ACTFL Can-Do Statements Artifacts Demonstrating My Intercultural Competence Artifact Name, Course 2-3 sentence description in English that explains what you did for the assignment and a reflection that focuses on the growth, skills or perspective you gained. Artifact Name, Course 2-3 sentence description in English that explains what you did for the assignment and a reflection that focuses on the growth, skills or perspective you gained.	pressbooks	2025-03-22T05:08:52.751487	12-17-2024	{ "license": "Creative Commons Zero - Public Domain - https://creativecommons.org/publicdomain/zero/1.0/", "url": "https://boisestate.pressbooks.pub/world300template/chapter/intercultural-competency/", "book_url": "https://boisestate.pressbooks.pub/world300template/front-matter/about-this-template/", "title": "An ePortfolio Template for Language Majors", "author": "Amber Hoye", "institution": "Boise State University", "subject": "Language teaching and learning, Language learning for specific purposes, Language learning for business, professional and vocational, Language learning for academic, technical and scientific purposes, Education, Careers guidance, Advice on careers and achieving success, Job hunting / changing careers" }
https://boisestate.pressbooks.pub/world300template/chapter/presentational-speaking/	Language Proficiency Presentational Speaking (Remove these instructions after reading) Instructions: Replace the placeholder artifacts and the descriptions with your own content. What is Presentational Communication? Artifacts Demonstrating My Presentational Speaking Proficiency Artifact Name, Course 2-3 sentence description in English that explains what you did for the assignment and a reflection that focuses on the growth, skills or perspective you gained. Insert artifact Artifact Name, Course 2-3 sentence description in English that explains what you did for the assignment and a reflection that focuses on the growth, skills or perspective you gained. Insert artifact	pressbooks	2025-03-22T05:08:52.759841	12-17-2024	{ "license": "Creative Commons Zero - Public Domain - https://creativecommons.org/publicdomain/zero/1.0/", "url": "https://boisestate.pressbooks.pub/world300template/chapter/presentational-speaking/", "book_url": "https://boisestate.pressbooks.pub/world300template/front-matter/about-this-template/", "title": "An ePortfolio Template for Language Majors", "author": "Amber Hoye", "institution": "Boise State University", "subject": "Language teaching and learning, Language learning for specific purposes, Language learning for business, professional and vocational, Language learning for academic, technical and scientific purposes, Education, Careers guidance, Advice on careers and achieving success, Job hunting / changing careers" }
https://boisestate.pressbooks.pub/world300template/chapter/presentational-writing/	Language Proficiency Presentational Writing (Remove these instructions after reading) Instructions: Replace the placeholder artifacts and the descriptions with your own content. What is Presentational Communication? - Creation of messages to inform, explain, persuade, or narrate - One-way communication intended to facilitate interpretation by members of the other culture where no direct opportunity for the active negotiation of meaning between members of the two cultures exists - To ensure the intended audience is successful in its interpretation, the “presenter” needs knowledge of the audience’s language and culture – ACTFL Proficiency Guidelines My Presentational Writing Proficiency Rating Date of Completion: Assessment Results: Artifacts Demonstrating My Presentational Writing Proficiency Artifact Name, Course 2-3 sentence description in English that explains what you did for the assignment and a reflection that focuses on the growth, skills or perspective you gained. Insert artifact Artifact Name, Course 2-3 sentence description in English that explains what you did for the assignment and a reflection that focuses on the growth, skills or perspective you gained. Insert artifact Artifact Name, Course 2-3 sentence description in English that explains what you did for the assignment and a reflection that focuses on the growth, skills or perspective you gained. Insert artifact	pressbooks	2025-03-22T05:08:52.769418	12-17-2024	{ "license": "Creative Commons Zero - Public Domain - https://creativecommons.org/publicdomain/zero/1.0/", "url": "https://boisestate.pressbooks.pub/world300template/chapter/presentational-writing/", "book_url": "https://boisestate.pressbooks.pub/world300template/front-matter/about-this-template/", "title": "An ePortfolio Template for Language Majors", "author": "Amber Hoye", "institution": "Boise State University", "subject": "Language teaching and learning, Language learning for specific purposes, Language learning for business, professional and vocational, Language learning for academic, technical and scientific purposes, Education, Careers guidance, Advice on careers and achieving success, Job hunting / changing careers" }
https://boisestate.pressbooks.pub/world300template/chapter/chapter-1/	Language Proficiency Interpersonal Speaking What is Interpersonal Communication? - Active negotiation of meaning among individuals - Participants observe and monitor one another to see how their meanings and intentions are being communicated - Adjustments and clarifications are made accordingly - Speaking and listening (conversation); reading and writing (text messages or via social media) – ACTFL Proficiency Guidelines My Interpersonal Speaking Proficiency Rating Date of Completion: Assessment Results:	pressbooks	2025-03-22T05:08:52.778130	12-17-2024	{ "license": "Creative Commons Zero - Public Domain - https://creativecommons.org/publicdomain/zero/1.0/", "url": "https://boisestate.pressbooks.pub/world300template/chapter/chapter-1/", "book_url": "https://boisestate.pressbooks.pub/world300template/front-matter/about-this-template/", "title": "An ePortfolio Template for Language Majors", "author": "Amber Hoye", "institution": "Boise State University", "subject": "Language teaching and learning, Language learning for specific purposes, Language learning for business, professional and vocational, Language learning for academic, technical and scientific purposes, Education, Careers guidance, Advice on careers and achieving success, Job hunting / changing careers" }
https://boisestate.pressbooks.pub/world300template/chapter/interpretive-reading-listening-proficiency/	Language Proficiency Reading & Listening - Learners understand, interpret, and analyze what is heard, read, or viewed on a variety of topics. – ACTFL Proficiency Guidelines My Reading Proficiency Rating Date of Completion: Assessment Results: My Listening Proficiency Rating Date of Completion: Assessment Results:	pressbooks	2025-03-22T05:08:52.786561	12-17-2024	{ "license": "Creative Commons Zero - Public Domain - https://creativecommons.org/publicdomain/zero/1.0/", "url": "https://boisestate.pressbooks.pub/world300template/chapter/interpretive-reading-listening-proficiency/", "book_url": "https://boisestate.pressbooks.pub/world300template/front-matter/about-this-template/", "title": "An ePortfolio Template for Language Majors", "author": "Amber Hoye", "institution": "Boise State University", "subject": "Language teaching and learning, Language learning for specific purposes, Language learning for business, professional and vocational, Language learning for academic, technical and scientific purposes, Education, Careers guidance, Advice on careers and achieving success, Job hunting / changing careers" }
https://boisestate.pressbooks.pub/world300template/chapter/resume/	My Skills Résumé (Remove these instructions and the placeholder resumes after reading) Instructions: Add your Résumé or CV to this page using the add media button as a JPEG or PNG preview and a PDF link to download using the example below. Be sure to remove any sensitive information from the document such as your phone number or address.	pressbooks	2025-03-22T05:08:52.793853	12-17-2024	{ "license": "Creative Commons Zero - Public Domain - https://creativecommons.org/publicdomain/zero/1.0/", "url": "https://boisestate.pressbooks.pub/world300template/chapter/resume/", "book_url": "https://boisestate.pressbooks.pub/world300template/front-matter/about-this-template/", "title": "An ePortfolio Template for Language Majors", "author": "Amber Hoye", "institution": "Boise State University", "subject": "Language teaching and learning, Language learning for specific purposes, Language learning for business, professional and vocational, Language learning for academic, technical and scientific purposes, Education, Careers guidance, Advice on careers and achieving success, Job hunting / changing careers" }
https://boisestate.pressbooks.pub/world300template/chapter/myskills/	My Skills My Skills (Remove these instructions and the placeholder skill inventory after reading) Refer to the Skills Inventory assignment you completed early in the semester. You will, in the format of your choosing (and please feel free to get creative), highlight your skills on your ePortfolio. You may choose to share only the skills that you have mastered or add those that you are working on/would like to gain in the future too. Please be sure to highlight the skills you have developed and honed as a language major. Ideas for Highlighting Your Skills - Create a word cloud and include a written or oral reflection on your strengths and language related skills. - Create an infographic or self-paced presentation using Google Slides or Canva - Create a one minute video using a tool like Canva or Adobe Express where you talk about your skills - Proficient in clear written and verbal communication, demonstrated through crafting compelling essays in French literature and actively engaging in discussions in French conversation groups. - Strong organizational skills, evidenced by efficiently managing schedules and projects with digital tools like Google Calendar and Trello. - Advanced abilities in analyzing complex texts and solving linguistic challenges, showcased by studying French poetry extensively and interpreting literary texts. - Collaborative aptitude, exemplified by participation in group projects such as creating presentations on French culture. - Technological proficiency, utilizing online resources for linguistic research and creating visually appealing presentations with software like Canva and PowerPoint. - Leadership capabilities, demonstrated by initiating thoughtful discussions in linguistics seminars and leading activities in French Club to foster a supportive learning environment for peers.	pressbooks	2025-03-22T05:08:52.802900	12-17-2024	{ "license": "Creative Commons Zero - Public Domain - https://creativecommons.org/publicdomain/zero/1.0/", "url": "https://boisestate.pressbooks.pub/world300template/chapter/myskills/", "book_url": "https://boisestate.pressbooks.pub/world300template/front-matter/about-this-template/", "title": "An ePortfolio Template for Language Majors", "author": "Amber Hoye", "institution": "Boise State University", "subject": "Language teaching and learning, Language learning for specific purposes, Language learning for business, professional and vocational, Language learning for academic, technical and scientific purposes, Education, Careers guidance, Advice on careers and achieving success, Job hunting / changing careers" }
https://ecampusontario.pressbooks.pub/arrivalactivities/chapter/what-are-arrival-activities/	1 What are Arrival Activities? Arrival activities are brief non-assessed opportunities designed to foster community, develop connections, gather feedback, and enhance student engagement during the opening moments of class. (Note that these activities pertain to the start of all classes, and not just the first class of term.) Rather than jumping right into a formal lecture or discussion, they are “start-before-the-start” activities that can stand alone from course content.[1] Arrival activities serve various functions within the classroom environment. For example, they can invite students to engage with the course; act as wellness check-ins; gather feedback from students; acclimatize students to what participation and engagement might look like in a digital learning environment; and provide opportunities for connection and perspective-taking. We can think of arrival activities in a few different ways. Students arrive at our classrooms (virtual or face-to-face) from another class, work, or maybe standing in line for coffee and chatting with friends. So firstly, arrival activities can help learners bring their attention to the course. Secondly, arrival activities can potentially activate and bring forth students’ existing knowledge and experiences related to the course content. Activating prior knowledge is one way to enhance enduring learning and creates opportunities for the co-construction of knowledge. Arrival activities are also one way to bring course learning objectives (think Bloom here), like the application of knowledge, to the foreground. By linking and bridging current course content with current events, a brief video, or an assigned reading, students have the opportunity to reflect (in a meta-cognitive moment), to frame, and contextualize the information about to be learned. Arrival activities can also be used to gauge where the students are at (re: mental wellness) and offer them a chance to decompress from their last class. For example, breathing exercises and questions related to amount of sleep, amount of time sitting in front of a computer, etc. can help inform your decisions regarding what to present in class that day. - Huggett, C. (2017). 5 Ways to Open an Interactive Virtual Class. Association for Talent Development. Retrieved from https://www.td.org/insights/5-ways-to-open-an-interactive-virtual-class. ↵	pressbooks	2025-03-22T05:08:52.812399	10-18-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/arrivalactivities/chapter/what-are-arrival-activities/", "book_url": "https://ecampusontario.pressbooks.pub/arrivalactivities/front-matter/arrival-activities/", "title": "Arrival Activities", "author": "Co-designed by students, recent graduates, educational developers, librarians,, instructors", "institution": "University of Toronto, Mississauga, Western University, York University, Trent University, University of Guelph", "subject": "Education, Open learning, distance education" }
https://ecampusontario.pressbooks.pub/arrivalactivities/chapter/arrival-activities-sparking-student-engagement-fostering-community/	2 Arrival Activities: Sparking Student Engagement & Fostering Community Arrival activities spark student engagement and can help to build a sense of community in the classroom. When we say student engagement, we mean “students’ willingness and desire to contribute and be successful in a learning process that leads them to higher-level thinking and long-term understanding.”[1] A student’s willingness can have several facets: “engagement requires a psychological investment on the part of the learner as well as persistence in undertaking the learning task…Additionally, several interconnected factors such as motivation, attention, involvement, and intellectual effort can support engagement.”[2] It’s important to note that both cognitive and emotional engagement are involved in fostering engaged student behaviour, and that instructors play a role in facilitating this engagement. Cognitive Engagement: Students tend to be aware of the importance of being cognitively engaged with course material. Blakey and Major[3] carried out an open-ended online survey about engagement with a group of forty students, most of whom were enrolled full-time in a graduate education program. In this study, students indicated that they felt learner engagement must be active; students pointed to the importance of their own involvement in their learning, as well as to the concept of intellectual effort more generally. As one student explained, learners “must be involved in the learning process,” and another student commented that, as opposed to “passively sitting in a classroom”, engagement involves students’ participation in thoughtful discussion, as well as “[going] above and beyond on assignments in order to learn.”[4] Students also suggested that engagement involves being “fully invested in the learning process, whether by participating in classroom discussions with faculty and classmates, completing reading assignments or other projects outside of class time, or by generally committing to the class.”[5] Active learning, participation in activities and discussion, and applying effort to learning can all be significant components in students’ cognitive engagement (bearing in mind that engaged investment in course activities or discussions may appear differently or take different forms for different learners, including neurodiverse students). Emotional Engagement: Student engagement also involves feeling connected with a course (and with the learning material) on an emotional level. Students have recognized the need for learners to have “a positive view of the course and of the importance of learning,” to believe that they can succeed, and to want to learn.[6] The emotional aspect of engagement, considered in addition to the cognitive aspect, indicates that engagement overall is “the level of involvement of students personally, emotionally, and academically pertaining to a course both in and out of the classroom.”[7] “During my BSc, I was the most emotionally invested in a class that was taught by a professor I was working with for my thesis. I realized that I was so invested in the class because I had a working relationship with the instructor, and it made me appreciate his jokes, humour, added activities, and bonus work. If we can give students that type of connection to their professor, they will likely be more engaged.” Former BSc student Community refers to the sense of belonging and membership within a group.[8] A course community is centered around the collaborative learning that will take place, which can be seen in how students, instructor(s), and teaching assistants interact, work, and support each other in pursuit of the course learning goals.[9] Implementing strategies to cultivate a sense of community in your course can not only increase classroom participation and deepen learning,[10] it also contributes to a student’s well-being, which ultimately promotes learning.[11] Arrival activities are one example of teaching strategies that can foster both engagement and community. Thoughtfully and intentionally planning activities that welcome students to each class can help set and keep the tone for the type of collaborative and supportive atmosphere instructors want to provide to their learners. - Blakey, C.H., & Major, C. (2019). Student Perceptions of Engagement in Online Courses: An Exploratory Study. Online Journal of Distance Learning Administration, 22(4), n4. ↵ - Blakey, C.H., & Major, C. (2019). Student Perceptions of Engagement in Online Courses: An Exploratory Study. Online Journal of Distance Learning Administration, 22(4), n4. ↵ - Blakey, C.H., & Major, C. (2019). Student Perceptions of Engagement in Online Courses: An Exploratory Study. Online Journal of Distance Learning Administration, 22(4), n4. ↵ - Blakey, C.H., & Major, C. (2019). Student Perceptions of Engagement in Online Courses: An Exploratory Study. Online Journal of Distance Learning Administration, 22(4), n4. ↵ - Blakey, C.H., & Major, C. (2019). Student Perceptions of Engagement in Online Courses: An Exploratory Study. Online Journal of Distance Learning Administration, 22(4), n4. ↵ - Blakey, C.H., & Major, C. (2019). Student Perceptions of Engagement in Online Courses: An Exploratory Study. Online Journal of Distance Learning Administration, 22(4), n4. ↵ - Blakey, C.H., & Major, C. (2019). Student Perceptions of Engagement in Online Courses: An Exploratory Study. Online Journal of Distance Learning Administration, 22(4). ↵ - Yuan, J., & Kim, C. (2014). Guidelines for facilitating the development of learning communities in online courses. Journal of Computer Assisted Learning, 30(3), 220-232. http://dx.doi.org/10.1111/jcal.12042. ↵ - Berry, S. (2017). Building community in online doctoral classrooms: Instructor practices that support community. Online Learning, 21(2), 1-22. http://dx.doi.org/10.24059/olj.v21i2.875. ↵ - Garrison, D.R., Anderson, T., & Archer, W. (2010). The first decade of the community of inquiry framework: A retrospective. The internet and higher education, 13(1-2), 5-9. ↵ - Keyes, C.L.M., Eisenberg, D., Perry, G.S., Dube, S.R., Kroenke, K., & Dhingra, S.S. (2012). The relationship of level of positive mental health with current mental disorders in predicting suicidal behavior and academic impairment in college students. Journal of American College Health, 60(2), 126-133. ↵	pressbooks	2025-03-22T05:08:52.825599	10-18-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/arrivalactivities/chapter/arrival-activities-sparking-student-engagement-fostering-community/", "book_url": "https://ecampusontario.pressbooks.pub/arrivalactivities/front-matter/arrival-activities/", "title": "Arrival Activities", "author": "Co-designed by students, recent graduates, educational developers, librarians,, instructors", "institution": "University of Toronto, Mississauga, Western University, York University, Trent University, University of Guelph", "subject": "Education, Open learning, distance education" }
https://ecampusontario.pressbooks.pub/arrivalactivities/chapter/why-are-arrival-activities-important/	3 Why are Arrival Activities Important? Research suggests that arrival activities deepen student engagement, strengthen motivation, build student enthusiasm, and foster community through social learning. In addition, these types of activities can increase feelings of connectedness between the instructor and students, and assist in providing the instructor with feedback on student experiences and the course’s progress and impact. Since we have highlighted student engagement and activating emotional investment earlier in this work, we are going to emphasize a few other impacts of arrival activities below. Sharpens Active Learning Skills Arrival activities have been found to create more time for instructors to focus on active learning and the active engagement of students by preparing them to engage with and connect to course content before class begins. Moravec and colleagues[1] found that “learn before lecture (LBL)” assignments, in combination with interactive exercises, helped increase student engagement and content understanding. Arrival activities can also be effective at helping students acquire new skills. Sharples[2] observed that students could develop a variety of skills from gaming (complex digital games) as an arrival activity. These skills included strategy and problem-solving, understanding consequences, coordination, and teamwork – all of which are important skills for students to develop during their time in higher education. Foster Community through Social Learning Arrival activities also facilitate community through social learning. For example, game-enhanced learning (GEL) has been known to increase student performance and engagement.[3] Serious games, as a branch of video games designed for purposes other than solely entertainment, have been found to have major applications in “education and training, engineering, healthcare, military applications, city planning, production, crisis response” and other fields.[4] Finally, gamification and serious gaming have an important relationship to social learning, which is key to improving educational practices and infrastructure in the future.[5] Arrival activities are important to the social learning and development of students for a number of reasons, as outlined above, and they can serve as useful functions in an online course. Showing Genuine Care Pedagogies of kindness[6][7] and care are core to establishing feelings of connection amongst students. One way that instructors can signpost care is by showing a genuine interest in the student experience and by gathering feedback to increase instructor awareness of the students’ situations. Arrival activities can help create a feeling of dialogue and perspective-taking when contributions are shared anonymously. - Moravec, M., Williams, A., Aguilar-Roca, N., & O'Dowd, D. K. (2010). Learn before Lecture: A Strategy That Improves Learning Outcomes in a Large Introductory Biology Class. CBE - Life Sciences Education, 9(4), 473-481. https://doi.org/10.1187/cbe.10-04-0063. ↵ - Sharples, M. (2019). Learning from gaming. In Practical Pedagogy (1st Ed). Routledge. https://doi.org/10.4324/9780429485534. ↵ - Charles, T., Bustard, D., & Black, M. (2011). Experiences of Promoting Student Engagement Through Game-Enhanced Learning. In Ma, M., Oikonomou, A., Jain, L. (Eds.), Serious Games and Edutainment Applications (pp. 425-445). Springer, London. https://doi.org/10.1007/978-1-4471-2161-9_21. ↵ - Ma, M., Oikonomou, A., & Jain, L. (2011). Serious Games and Edutainment Applications. Springer, London. https://doi.org/10.1007/978-1-4471-2161-9_1. ↵ - de Freitas S. & Liarokapis F. (2011). Serious Games: A New Paradigm for Education? In Ma, M., Oikonomou, A., Jain, L. (Eds.), Serious Games and Edutainment Applications (pp. 9-23). Springer, London. https://doi.org/10.1007/978-1-4471-2161-9_2. ↵ - Rawle F. (2021). A Pedagogy of Kindness: the Cornerstone for Student Learning and Wellness. Times Higher Education. Retrieved from https://www.timeshighereducation.com/campus/pedagogy-kindness-cornerstone-student-learning-and-wellness. ↵ - Denial, C. (2019) A Pedagogy of Kindness. Hybrid Pedagogy. Retrieved from https://hybridpedagogy.org/pedagogy-of-kindness/. ↵	pressbooks	2025-03-22T05:08:52.837479	10-18-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/arrivalactivities/chapter/why-are-arrival-activities-important/", "book_url": "https://ecampusontario.pressbooks.pub/arrivalactivities/front-matter/arrival-activities/", "title": "Arrival Activities", "author": "Co-designed by students, recent graduates, educational developers, librarians,, instructors", "institution": "University of Toronto, Mississauga, Western University, York University, Trent University, University of Guelph", "subject": "Education, Open learning, distance education" }
https://ecampusontario.pressbooks.pub/arrivalactivities/chapter/types-of-arrival-activities/	4 Types of Arrival Activities Arrival activities fall into several broad categories: icebreakers, community builders, energizers, content-based activities, and gaming. They can be blended in order to bolster engagement, foster community, and build investment in the class. Arrival activities are purposefully low effort “asks” designed to orient learners to the learning environment. The wide range of possible arrival activities gives instructors the ability to choose those that would best fit their class and benefit their students. However, all arrival activities serve a common purpose: a ‘starting point’ to immediately capture the attention and engagement of students, to connect students to the course material (and to each other), and to set the stage for the class to follow. Icebreakers are a type of arrival activity that have been “designed to help students begin to feel comfortable with each other… characterized by brevity, relatively low-risk involvement, and participation” from all members of the class.[1] These activities encourage connections between students who may not have known each other before the course. They also help to create a sense of comfort with the class itself, and with the concept of experiential learning. Icebreaker questions could be related to the course content (e.g., “which course topic looks most interesting to you and why?”), help inform your teaching (e.g., “what question(s) do you have about the course outline?”), or help learners to get better acquainted, build community, and have fun (e.g., “share one boring fact about yourself”). “I met one of my best friends through an icebreaker in third year university. Without the icebreaker, we may have never begun talking” Former BSc student. Community builders are arrival activities that are “designed to help students to develop a sense of connection and shared focus in a designated group. They are characterized by an emphasis on one or more group variables: e.g., roles, norms, purposes, processes, leadership, or dynamics.”[2] Typically, a group builder activity will take up more class time than an icebreaker, and will require more intentional facilitating on the part of the course instructor. Energizers (or “Re-energizers”) are a type of arrival activity that are “designed to help students to ‘clear their minds’, to re-create personal and group energy, or for transition purposes.”[3] They are typically quick, rely on participant involvement, and are known for their sense of ‘fun’. These ‘fun’ activities have a positive impact on students’ participation, motivation, and enthusiasm for the course. An example might be setting time aside at the start of class for groups to create a team charter ahead of a group project, which would help provide a roadmap for their work – setting meeting times, contact methods, preferred collaborative technologies, roles/tasks and project timelines. These could all be part of one group builder activity or scaffolded over a few classes. Course Content-Based Arrival Activities are an opportunity to reinforce core themes and course sticking points, often through active learning.[4] For example, “entry ticket” activities invite students to individually respond to a low-effort, content-based question at the beginning of class[5]; “brainwriting” tasks allow students to generate ideas in response to a prompt provided to them as they enter the room for later discussion[6]; and “one-minute papers” ask students to write for one minute about what they have learned in the course or what they are having trouble with thus far.[7] Content-based arrival activities can also connect course content and themes to popular culture and contemporary news items. For example, students may be asked to upload live reactions to a piece of media played at the beginning of the class, or to write a short low-stakes reflection on the piece.[8] Games and Internet-based activities don’t necessarily have to be content-based, but they should be engaging and low-stakes for students.[9] Some examples include trivia or Jeopardy-style games, and content-based games. Games may be pre-existing or instructor-created, and can function as a tool that gives students “a novel way to interact with games in order to learn skills and knowledge”; they also support social-emotional development, an important factor in learning.[10] In addition to games, internet-based activities (for example, an Internet scavenger hunt related to course material) and social media (such as Facebook or Twitter) may be used in arrival activities.[11] Social media arrival activities could include group forum discussions, or creating and sharing posts (content-based or open-ended) to a specific hashtag.[12][13] However, course designers should bear in mind when planning activities that open-ended sharing can have the risk of leading to hurtful or inappropriate content being posted. - Boatman, S. A. (1991, April). Icebreakers and group builders for the classroom [Paper presentation]. Annual Meeting of the Central States Communication Association, Chicago, IL, United States. ↵ - Boatman, S. A. (1991, April). Icebreakers and group builders for the classroom [Paper presentation]. Annual Meeting of the Central States Communication Association, Chicago, IL, United States. ↵ - Boatman, S. A. (1991, April). Icebreakers and group builders for the classroom [Paper presentation]. Annual Meeting of the Central States Communication Association, Chicago, IL, United States. ↵ - Brame, C. J. (2016). Active Learning. Vanderbuilt University Center for Teaching. Retrieved from https://cft.vanderbilt.edu/guides-sub-pages/active-learning/. ↵ - Brown University. (2022). Interactive Classroom Activities. Retrieved from https://www.brown.edu/sheridan/teaching-learning-resources/teaching-resources/classroom-practices/active-learning/interactive. ↵ - Whenham, T. (2020). 15 active learning activities to energize your next college class. Nureva. Retrieved from https://www.nureva.com/blog/education/15-active-learning-activities-to-energize-your-next-college-class. ↵ - Whenham, T. (2020). 15 active learning activities to energize your next college class. Nureva. Retrieved from https://www.nureva.com/blog/education/15-active-learning-activities-to-energize-your-next-college-class. ↵ - Whenham, T. (2020). 15 active learning activities to energize your next college class. Nureva. Retrieved from https://www.nureva.com/blog/education/15-active-learning-activities-to-energize-your-next-college-class. ↵ - Sharples, M. (2019). Learning from gaming. In Practical Pedagogy (1st Ed). Routledge. https://doi.org/10.4324/9780429485534. ↵ - Ma, M., Oikonomou, A., & Jain, L. (2011). Serious Games and Edutainment Applications. Springer, London. https://doi.org/10.1007/978-1-4471-2161-9_1. ↵ - Sharples, M. (2019). Learning through social media. In Practical Pedagogy (1st Ed). Routledge. https://doi.org/10.4324/9780429485534. ↵ - Sharoff, L. (2019). Creative and Innovative Online Teaching Strategies: Facilitation for Active Participation. Journal of Educators Online, 16(2), 2. https://doi.org/10.9743/jeo.2019.16.2.9. ↵ - Whenham, T. (2020). 15 active learning activities to energize your next college class. Nureva. Retrieved from https://www.nureva.com/blog/education/15-active-learning-activities-to-energize-your-next-college-class. ↵	pressbooks	2025-03-22T05:08:52.851056	10-18-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/arrivalactivities/chapter/types-of-arrival-activities/", "book_url": "https://ecampusontario.pressbooks.pub/arrivalactivities/front-matter/arrival-activities/", "title": "Arrival Activities", "author": "Co-designed by students, recent graduates, educational developers, librarians,, instructors", "institution": "University of Toronto, Mississauga, Western University, York University, Trent University, University of Guelph", "subject": "Education, Open learning, distance education" }
https://ecampusontario.pressbooks.pub/arrivalactivities/chapter/advice-on-how-to-incorporate-arrival-activities-into-your-class/	5 Advice on How to Incorporate Arrival Activities into Your Class When planning and building arrival activities: - Use the activity to set the tone that you would like to maintain for the rest of the session. - Test out your instructions on someone else – someone not as tech-familiar – and on multiple operating systems. - Imagine yourself entering a room about which you have no information. Who will be there? What will you be expected to do? Where will you ‘sit’? Then, build an activity that would put you at ease. You may also want to include a short welcome note, small instructions, or name tags – all of these elements can be translated into a remote class or an in-person class. - Plan to change it up – don’t always start with the same type of activity. - Formative feedback is a wonderful way to engage in arrival activities. You could end one class/lesson with formative feedback (e.g., start-stop-continue, one thing I learned/muddiest moment, if I could revisit 2 ideas from class today, I would like to learn more about) and then begin the next class with a summary. This system allows you to re-engage with important ideas that students have asked to hear more about, and lets you know if they have missed any main ideas. Using formative feedback as bridge-in allows students to bring their existing knowledge and experience to bear on the information about to be learned and have the chance to see how ideas/details are connected – seeing the forest for a few minutes before working with the trees. - Arrival activities are also really useful in a lab setting (especially for first- and second-year classes where the lecture size is often over 500 people). In the lab, we usually have 30-40 students who are getting to know each other. Creating group-based arrival activities in these scenarios will help the students get to know each other and find friendly faces. For example, asking the students to solve a puzzle or a rhyme allows them to work together on an activity that isn’t too serious. - Get students involved – a ‘group builder’ activity could be assigning groups to create and facilitate a meaningful icebreaker for each of the class meeting times. This creates additional motivation to engage in the activities. - Take a mid-term barometer check on how the arrival activities are landing. This in itself could be an arrival activity, framed as: “Each week our class starts with an arrival activity. How helpful are these to your learning? What would you suggest I start doing? What would you suggest I stop doing? What would you suggest I continue doing?” When introducing arrival activities to your students: - Explain to the class what arrival activities are, what their purpose is, and why you have chosen to include them at the beginning of the class. That way, the students know what to expect when they come to class. - Clearly communicate instructions in multiple ways – a slide, verbally, and if applicable, in the chat. This is particularly helpful if students arrive late (and they do!) and need to get up to speed quickly on what’s going on. - Provide instructions not just for what to do, but also how to do it. For example, if you are asking students to annotate an image, give them the technical steps for how to do this. - It is hard sometimes to give over class time to arrival activities, but there is plenty of evidence that supports the ways in which they promote enduring learning. Arrival activities are not busy work if they are designed specifically and intentionally. In your class, frame and provide the context and purpose of these activities, explain that they are for student to use what they already know, no matter how vast or little, and for them to think about the course content. Let students know that these activities are important for giving context to their learning – that you want them to apply, synthesize, understand, categorize, and put the information/facts/ideas that they learn into context, which they can then use in a variety of different ways. Arrival activities can also highlight how knowledge/ideas/statistical information can be framed differently based off of students’ diverse and varied knowledges and experiences. - Student reluctance to participate can be framed as a desirable difficulty – discuss how learning is challenging and requires effort, but that these activities are designed to enhance enduring learning. - Research highlights the importance of intellectual relationships in learning, and how small study groups support long learn learning and student retention. However, active learning and group work are often seen as barriers to grades rather than as integral to long-term learning. It is helpful for students to know why you want them to explore and discuss together. During the arrival activity: - Monitor the activity as it is happening and intervene if necessary. - Invite students to identify themselves as the creator of a specific contribution if this contribution becomes the subject of the discussion, but do not force it. - Allow students to engage however they want. For example, if you provide a colouring page, recognize that some will colour out of the lines, or doodle on the margin. Give them that space. It will be messy. - Depending on the activity, engage with students while they are doing it. It helps build a positive rapport with students and creates a welcoming, relaxed environment that isn’t directly related to the course. Following the arrival activity: - Keep a record or notes on levels of student engagement. In the remote Zoom room, you could take a screenshot or keep a recording of the activity. You could then paste this screenshot into your slides so that when you review them the next time that you teach, you can make adjustments based on how students engaged. - If students are asked to provide a response, try to incorporate that response into your next lecture. Students will feel heard – that something is actually happening with the information that they are providing. - Gather feedback about the activities. You can ask students which ones they prefer and ask for suggestions, or allow students to choose which kinds of activities you include going forward. - Be prepared that some of the activities are not going to be the success you envision. It is very rare to have all of them meet the outcomes you anticipate. That doesn’t mean it’s not worth doing – switch it up and try something different. When they hit the mark it is worth it!	pressbooks	2025-03-22T05:08:52.863254	10-18-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/arrivalactivities/chapter/advice-on-how-to-incorporate-arrival-activities-into-your-class/", "book_url": "https://ecampusontario.pressbooks.pub/arrivalactivities/front-matter/arrival-activities/", "title": "Arrival Activities", "author": "Co-designed by students, recent graduates, educational developers, librarians,, instructors", "institution": "University of Toronto, Mississauga, Western University, York University, Trent University, University of Guelph", "subject": "Education, Open learning, distance education" }
https://ecampusontario.pressbooks.pub/arrivalactivities/chapter/some-notes-about-inclusion-and-accessibility/	6 The Importance of Inclusion and Accessibility There are many possible arrival activities that can foster active learning. The activities you select will depend very much on the actual students in the class. Most activities can be modified once you know who your students are and where your class will take place. Asking ‘what’s one thing you would like me to know about you as a learner’ as an initial arrival activity can be helpful. You may also want to think about scaffolding activities, building up to them throughout the course, and conducting these activities in small groups rather than in front of a lecture hall. The point is to find out what your students need and who they are. The following are important notes to keep in mind regarding inclusion and accessibility: - All videos should have closed captions that have been checked for accuracy. - If the activities take place before the officially timetabled start of class, do not count them as regular course work. - Be aware of the physical space of your class ahead of time and how students with disabilities might be best supported and accommodated with various activities – moving chairs and desks around can be a navigation or anxiety-inducing barrier for students. If a class is set up as an ‘active learning classroom’ with multiple tables/stations, considering making sure each table has an open spot for students in a wheelchairs or those with service animals. You may also consider posting activities on a learning management system to avoid asking students to move around the room. - Different identities along the axis of disability, race, gender, etc., may be visible, while others may be less visible. It is important that students are not asked to disclose diagnoses, etc. As the instructor, you should be clear that individuals do not have to share personal and private information, and you should know what to do when someone makes an error and asks an inappropriate question. To ensure that you are meeting everyone’s accessibility and inclusion needs, solicit information regarding student needs anonymously – this helps to avoid a person with a visible disability having to tell everyone their own personal needs or speak for ‘all disabled’ people. If gathering this information online, it is important to remember that contributions are not necessarily anonymous unless you adjust your settings. Finally, it is important to let students know whether they will be identified as the authors of contributions or not. - Taking time at the start of class to have your arrival activity double as a “feedback session” can go a long way in establishing a sense of mutual trust and respect in your course. If students feel you are listening, and you are co-creating an experience together that’s authentic, they will likely flag issues of accessibility and inclusion as a result of the openness fostered during these moments. - Note that many of the sample activities included in the package rely on sight (e.g. Boggle, word searches, map, colouring pages, etc.). If you are sharing your screen, the activity will not be accessible to students who use screen readers. Some activities can also be shared via an attachment dropped in the chat so that students who use screen readers can access the activity. Note that other activities (i.e., colouring pages) are only accessible to sighted students. It is important to be aware of this and use a variety of different arrival activities. Sharing your slides at the very start of class and ensuring images include alt text can also increase the accessibility of the activities. If your arrival activity relies on a single question prompt, you could share the prompt on a slide, add it to the chat, and read it out. Note that the attached sample arrival activities include slide descriptions that can be posted into the chat as well as alt text descriptions of all images. - It is important to consider that for many students (including those that are neurodivergent) interacting with peers and engaging with them (and the professor) is not a natural or simple task. For many of these students this means anxiety or frustration. Allow students to have the option to refrain from participation or better yet, offer personal support/guidance or separate activities that can be done individually. - It is also important to consider that for many neurodivergent students, the implementation of new things like arrival activities can make these students anxious due to a change in routine. If these activities are implemented suddenly, it would be beneficial to let these students know beforehand and offer them a “run-down” of what this new change will entail. - It is very important in online learning to have instructions on how to annotate the screen or perform other features in Zoom. Although many of us aren’t new to online learning, we can’t make the assumption that every professor has exposed their students to these features beforehand. - As many of us offer hybrid learning opportunities, consider how the activities will be received in both in-person and virtual environments. It can be challenging to do both well in one session, so consider alternating between activities that might be better suited in person versus online. Some notes about participation: - Make sure students know they don’t have to participate or engage in the activity. - Provide instructions in multiple formats (text on slide, verbally, chat, etc.) for more equitable understanding. - Provide multiple ways of contributing. If you can, let the student choose how they share their contribution (be it through Zoom annotation tools, chat, audio, video, etc.). It may mean that your slide doesn’t get the pretty stamps you were hoping for, but you will get way more shares from students if they can choose. Allow students to engage in ways that are meaningful to them. For example, a neurodiverse student may not appear to be engaged even though they are, or a student with a speech impairment may not want to speak aloud. - Consider physical space. Many students struggle with forward-facing activities if they need to be able to see or hear something behind them (for instance, any student that needs to see faces or read lips for comprehension). Students with mobility barriers (visible or not) can also find it difficult to turn around to engage with something occuring behind them. - For those teaching in-person, it is also important to encourage students to move seats in-between lectures. True class engagement and peer-interaction cannot occur if students stick to one spot for the entire semester and only interact with their close-by peers or friends. Moving seats also gives students the ability to interact equally with the professor who is usually situated at the front of the lecture hall. Fluidity in seating also gives some students who usually refrain from participation (because they are seated very far back or isolated in corners) the opportunity to become more engaged and interactive. Some notes about music: If you are including music as an aspect of your arrival activities, it may be a good idea to keep a few things in mind: - Provide a link if you are streaming videos or using materials from other sources. Sometimes it is preferable to go to the link to experience it, rather than through your shared screen. - If you are playing music, share the song information on a slide (including artist, title, and a link to the video with close captioning). - Do a quick check to make sure that the artist hasn’t engaged in racist/sexist/transphobic/homophobic/etc. behaviour. - Make sure that you are sharing music or art from a diversity of artists. - If you curate a playlist for the class, share it with the students and ask them to recommend new music. Perhaps set a theme (e.g. all Canadian artists). - If students send you recommendations, be sure to use them. - If you have musicians in the classroom, ask if they’d like to provide the music during arrival activities (or during breaks in the class). “I feel arrival activities may be a good way for students to bond over, talk about, and generally just share their ethnic backgrounds with each other. The academic setting does not usually invite students to talk openly or out-of-context-ly about their backgrounds. I had went nearly an entire semester unaware that my seat-neighbour was also an ethnic Albanian; it was a great feeling as this was the only other individual that I met at the university who shared the same background as me. I think it is important to share our backgrounds with each other (if comfortable in doing so) as it helps us each learn new things about each other and appreciate our differences. In courses like History, Politics, Geography, and even a few of my English courses, this can be extremely interestingly relevant in terms of gaining different perspectives and is often necessary to know in order to be sensitive to and aware of diverse cultural/ethnic relations with lecture content. To exemplify, speaking of the Israel-Palestine conflict in History or English classes warrants some cultural caution when contributing to class. As a personal example, I did not appreciate being accused of being an ultra-capitalist for critiquing historical communist events and so, I had to make it evident that I come from a communist dictatorship country.” -Former student	pressbooks	2025-03-22T05:08:52.878948	10-18-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/arrivalactivities/chapter/some-notes-about-inclusion-and-accessibility/", "book_url": "https://ecampusontario.pressbooks.pub/arrivalactivities/front-matter/arrival-activities/", "title": "Arrival Activities", "author": "Co-designed by students, recent graduates, educational developers, librarians,, instructors", "institution": "University of Toronto, Mississauga, Western University, York University, Trent University, University of Guelph", "subject": "Education, Open learning, distance education" }
https://ecampusontario.pressbooks.pub/arrivalactivities/chapter/arrival-activity-slide-deck/	7 Arrival Activity Slide Deck The associated slide deck contains a variety of arrival activities. We have focused on arrival activities that are used in an online context, wherein students reply by annotating the shared screen, or by participating in the chat, in a class back-channel, or through the course’s learning management system. However, many of the activities can be used for in-person classes as well. Note that the samples provided here are just the tip of the iceberg in terms of what arrival activities could be. You can access the associated slide deck, alternate activity list, and ALT TEXT summaries here.	pressbooks	2025-03-22T05:08:52.888490	10-18-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/arrivalactivities/chapter/arrival-activity-slide-deck/", "book_url": "https://ecampusontario.pressbooks.pub/arrivalactivities/front-matter/arrival-activities/", "title": "Arrival Activities", "author": "Co-designed by students, recent graduates, educational developers, librarians,, instructors", "institution": "University of Toronto, Mississauga, Western University, York University, Trent University, University of Guelph", "subject": "Education, Open learning, distance education" }

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.