Adaptive Instructional Systems. Design and Evaluation

Name: Adaptive Instructional Systems. Design and Evaluation | Third International Conference, AIS 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24-29, 2021, Proceedings, Part I
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Third International Conference, AIS 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24-29, 2021, Proceedings, Part I

Robert A. Sottilare Jessica Schwarz(Editor)

Springer (Publisher)

Published on 3. July 2021

XXIV, 633 pages

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Content

Intro
Foreword
HCI International 2021 Thematic Areas and Affiliated Conferences
Contents - Part I
Contents - Part II
Conceptual Models and Instructional Approaches for AIS
A Conceptual Model for Hybrid Adaptive Instructional and Assessment Systems
1 Introduction
2 Adaptive Instructional Systems
3 Balanced Assessment Systems
4 AIAS Conceptual Model
5 Conclusion
References
Designing Adaptive Blended Learning Experiences for Military Formal School Courses
1 Introduction
1.1 Adaptive Blended Learning Environments
2 The Adaptive Blended Learning Experience
2.1 Testing in Resident and Non-resident Courses
2.2 The Center of Gravity Analysis Adaptive Moodle Lesson
2.3 Blended Learning Strategy
3 Method
3.1 Participants
3.2 Materials
3.3 Procedure
4 Analysis
4.1 Instructor-Led Classroom Efficiency
4.2 Knowledge Test Performance Scores
4.3 Application Test
4.4 COG Adaptive Lesson Student Activity
4.5 Student Subjective Responses
5 Discussion
5.1 Future Directions for Adaptive Blended Learning Designs
References
Personalized Mastery Learning Ecosystems: Using Bloom's Four Objects of Change to Drive Learning in Adaptive Instructional Systems
1 Introduction
1.1 The Challenge
1.2 The Opportunity
1.3 Understanding Learning Ecosystems
2 Personalized Mastery Learning Ecosystem (PMLE)
2.1 From PMLS to PMLE
2.2 The PMLE Places the Learner at the Center
2.3 The PMLE Describes the Complex Interactions Among the Learner, All People, Processes, Data, and Networked Connections in the learner's Environment
2.4 PMLE Requires Learning Engineering
3 My Math Academy PMLE: A Learning Engineering Approach
3.1 My Math Academy and Bloom's Four Objects of Change
3.2 Applying Learning Engineering to My Math Academy PMLE
4 Conclusion
References
Towards a Unified Model of Gamification and Motivation
1 Introduction
1.1 Advances Within the Field of Gamification Research.
1.2 Reviewing Prior Gamification Research
2 Gamification Typology
2.1 The 5 Dimensions of Gamification
2.2 Game Elements
2.3 Implementation
3 Motivation
3.1 User Motivation Thematic Analysis
4 The Unified Gamification and Motivation (UGM) Model
5 Discussion
References
Designing Learning Experiences to Encourage Development of Critical Thinking Skills
1 Introduction
2 Critical Thinking Skills
3 Instructional Approach to CTS
4 Experiential Development of Critical Thinking Skills
4.1 Example Lesson: Analysis
4.2 Example Lesson: Evaluation
4.3 Example Lesson: Inference
5 Practical Requirements for Feasibility and Effectiveness
6 Conclusions
References
Learning the Cognitive Skill of Topographic Map Reading Through Adaptive, High-Repetition Training
1 Introduction
1.1 Theoretical Foundations
1.2 Repetition Training for Topographic Map Reading
2 Technical Approach
2.1 Participants
2.2 Materials
2.3 Training Procedure
2.4 Adaptive Training Elements
3 Results
3.1 Task Accuracy Over Training
3.2 Difficulty of the Training Task Across Regions
3.3 Engagement and Effort
4 Discussion
References
Learning Engineering as an Ethical Framework
1 Learning Engineering
1.1 An Introduction to Learning Engineering
1.2 A Learning Engineering Case Study: Adaptive Activities
2 Ethics in Education
3 Learning Engineering as an Ethical Framework
4 Conclusion
References
Teaching Reinforcement Learning Agents with Adaptive Instructional Systems
1 Introduction
2 Motivation
3 Comparative Analysis of Training Concepts
3.1 The Learner, Its Learning Mechanism and Priors
3.2 Needs Analysis and Training Objective
3.3 Training System
3.4 Operational Deployment
4 Adaptive Instructional System for Learner Agents
4.1 Defining the Components of an AIS
4.2 Reinforcement Learning by an AIS
4.3 Advantages of an AIS
4.4 Dependencies on the Learner's Capabilities
5 Towards a Domain Implementation
5.1 Task Learning
5.2 Data-Driven Task Learning
5.3 Concluding
6 Discussion and Future Directions
References
Designing and Developing AIS
SQLearn: A Browser Based Adaptive SQL Learning Environment
1 Introduction
2 Related Works
3 Scope of Work
3.1 System Design
3.2 Testing Platform
3.3 Web Browser Extension
4 Adaptive Logic
4.1 System Prompts
5 Pilot Study
6 Discussions
7 Conclusion
Appendix - A
References
Towards the Design of an Adaptive Presence Card and Trust Rating System for Online Classes
1 Introduction
2 Review of Related Literature
3 The Presence Card (PC) and Trust Rating (TR)
4 Testing Methods
4.1 Metrics Used for the PC and TR Prototype Testing
5 Results and Discussion
5.1 Survey Results
5.2 Design Considerations for an Adaptive Presence Card and Trust Rating
6 Conclusions and Future Work
References
Education, Ethical Dilemmas and AI: From Ethical Design to Artificial Morality
1 Introduction and Motivations
1.1 Ethical Dilemmas in Education: A Layered Approach
2 Beyond Tools: AI and Ethical Behavior
2.1 Ethics by Design: Forewarned is Forearmed
2.2 Artificial Morality: Towards Encoding Moral Value
3 Exploring the Challenges Behind the Case Study
3.1 The Educational Layer
3.2 The Personal Layer
3.3 The Social Layer
4 Awaking Awareness: From Physical to Ethical Sensors
5 Conclusions and Future Work
References
Formal Methods in Human-Computer Interaction and Adaptive Instructional Systems
1 Introduction
2 Formal Methods in Human-Computer Interaction
2.1 Usability and Formal Methods
2.2 Workflow
2.3 Interaction Properties
2.4 Formal Notations
2.5 Adaptive Human-Computer Interaction
3 Adaptive Instructional Systems
4 Conclusion
References
Automating Team Competency Assessment in Support of Adaptive Dynamic Simulations
1 Introduction
2 Overview of GIFT
3 Overview of Team Dimensional Training
4 Team Assessment in Practice
4.1 Analysis, Evaluation and Assessment in Adaptive Instruction
4.2 Operational Process for Manual Team Assessment
4.3 Operational Process for Automated Team Assessment
5 Research and Design Contributions to Automated Assessment
5.1 The Role of AI in Automated Assessment
5.2 Capturing Individual Contributions to Team Performance
5.3 Implementing a Scalable Model for Team Assessment
5.4 Assigning Team Roles and Responsibilities Dynamically
6 Recommended Next Steps
References
Towards the Design and Development of an Adaptive Gamified Task Management Web Application to Increase Student Engagement in Online Learning
1 Introduction
1.1 Context
1.2 Objective
1.3 Scope and Limitations
2 Related Literature
3 Design and Development
4 Discussion
4.1 Discussion on the Current Application
4.2 Discussion on Adaptability
5 Conclusion
References
Intelligence Augmentation for Educators, Training Professionals, and Learners
1 Introduction
2 Definitions
3 AI-Based Tools in Industry
3.1 AI in Manufacturing
3.2 AI in Healthcare
3.3 Augmented Reality (AR) in Industry
3.4 Further Augmentation to Process Complex Problems in Industry
4 Industry 4.0 Tools in Education
4.1 Industry 4.0 Vision for Teaching and Learning
4.2 Cost
4.3 Dealing with Change
4.4 Speed of Change
4.5 Ethics and Data Protection
5 Collaboration Model
5.1 Suggestions for Education
6 Conclusion
References
A Generic CbITS Authoring Tool Using xAPI
1 Introduction
2 AutoTutor
2.1 AutoTutor Lite
2.2 xAPI
3 Framework
3.1 User Interface
4 Conclusion
References
Intelligence Augmentation for Collaborative Learning
1 Introduction
1.1 Changes in Educational Goals
1.2 Intelligence Augmentation Must Evolve to Fit Educational Goals
1.3 Intelligence Augmentation and CSCL Fits Today's Goals
1.4 The Pandemic Highlighted Gaps
1.5 A New Vision for Intelligence Augmentation Can Build on CSCL
2 Framing Computer Support for Collaborative Learning
2.1 About CSCL
2.2 How CSCL Frames Opportunities for Intelligence Augmentation
3 Scenarios for Intelligence Augmentation for Collaborative Learning
3.1 Actionable Awareness of Discussion Patterns
3.2 Maintaining Student Effort Across a Sequence of Activities
3.3 Developing Skill in a Collaborative Learning Role
4 Discussion: Commonalities that Deserve Attention
4.1 Automate, Add Awareness, Assist and Augment
4.2 Context-Sensitive, Longitudinal and Hybrid
4.3 Responsible, Human-Centered AI
5 Conclusion
References
Designing Ethical Agency for Adaptive Instructional Systems: The FATE of Learning and Assessment
1 Introduction
2 Ethical Agency in Human and Nonhuman Agents
2.1 Metacognition and Machine Morality
3 Modeling Metaethical Traditions
4 Explainable Ethical Standards for Assessment
4.1 Fairness
4.2 Accountability
4.3 Transparency
4.4 Ethics
5 Conclusion
References
The Role of Participatory Codesign in a Learning Engineering Framework to Support Classroom Implementation of an Adaptive Instructional System
1 Introduction
1.1 Approaching Learning Engineering from an Ecological Systems Perspective
1.2 Using Participatory Codesign to Create My Math Academy Dashboards
2 Method
2.1 Participants
2.2 Materials
2.3 Procedure
2.4 Data Analysis
3 Results
3.1 Interviews
3.2 Participatory Codesign Workshop
4 Discussion
4.1 Implications for Educators
4.2 Making Insights Accessible, Meaningful, and Actionable for Dashboard Implementation
4.3 Conclusions
References
Scaling Adaptive Instructional System (AIS) Architectures in Low-Adaptive Training Ecosystems
1 Introduction
2 Adapting to the Learner in Training Ecosystems
3 Scaling Dimensions
4 Scaling Methods
4.1 Scaling to Increase Accessibility to Low-Adaptive Systems
4.2 Scaling Using AIS Interoperability Standards
4.3 Scaling to Reduce Workload via AIS Process Automation
4.4 Dynamic AIS Architectures for Real-Time Assessment and Intervention
4.5 Scaling Between Systems Using LTI Standards
4.6 Scaling Between Systems Sharing Intelligent Agents
4.7 Scaling Horizontally Through Content Understanding
4.8 Scaling Vertically Through Content Understanding
5 Next Steps
References
HyWorM: An Experiment in Dynamic Improvement of Analytic Processes
1 Introduction
1.1 RAMPAGE, GLOW, and WorM
1.2 HyGene + WorM = HyWorM
2 Key WorM Features
2.1 Pseudorandom Assignment of Evidence and Counterfactual Forecast Problem Presentation
2.2 Dynamic Timing Control
2.3 Writing and Reading Memoranda as a Method of Cross-Pollination
2.4 Dynamic Method Selection Based on the Analyst's Work Products
2.5 Customized Instructions Based on Previous Analyst Work
2.6 Explicit Warnings Based on the Detection of Errors in User Content
3 HyGene Theory as a Basis for Analyst Workflow Guidance
3.1 Primary WorM Mechanism
4 Conclusion
References
Investigating Adaptive Activity Effectiveness Across Domains: Insights into Design Best Practices
1 Introduction
2 Methods
3 Results
3.1 RQ 1: How Do the Adaptive Activities Affect Learning Estimates for Students?
3.2 RQ 2: How Do the Adaptive Activities Affect Mean Summative Assessment Scores for Students?
3.3 RQ 3: What Features of Courseware Influence the Effectiveness of the Adaptive Activities?
4 Conclusion
References
Croatian POS Tagger as a Prerequisite for Knowledge Extraction in Intelligent Tutoring Systems
1 Introduction
1.1 Research Motivation and Background
1.2 POS Tagging for Under-Resourced Morphologically Rich Languages
2 Related Research
3 The Proposed Architecture
3.1 Convolutional Neural Network Based Feature Extraction
3.2 Training of the Convolutional Neural Network
3.3 Attention Based Feature Extraction
3.4 Training of the Attention Based Neural Network
4 Results and Future Work
5 Conclusion
References
Evaluation of AIS
Evaluating the Question: The Instructor's Side of Adaptive Learning
1 Introduction
2 What Information is Provided by the Augmented Knowledge
3 Methodology
4 Defining Intelligence Augmentation for This Paper
5 Background on Adaptive Learning: The Context of Why This is Important
6 Why Choose These Particular Evaluation Methods?
7 Methods for Evaluating the Assessments
7.1 Validity
7.2 Reliability
7.3 Standardization
8 The Tool
9 Conclusion
10 Recommendation for a Review by an Artificial Intelligence
References
Why Not Go All-In with Artificial Intelligence?
1 Introduction
2 Reasons for Caution
2.1 Trust
2.2 System Change
2.3 Fairness
3 Case Study: Khan Academy
References
Core to the Learning Day: The Adaptive Instructional System as an Integrated Component of Brick-and-Mortar, Blended, and Online Learning
1 Introduction
2 Instructional Theory
2.1 Adaptive Instructional System Theory of Change
2.2 Blended Learning Theory of Change
3 Bringing Blended Learning and Adaptive Instructional Systems Together
3.1 Pedagogical Considerations
3.2 Impact of Setting
3.3 Toward a Congruent Theory of Change
4 Conclusion
References
Learner Characteristics in a Chinese Mathematical Intelligent Tutoring System
1 Introduction
2 Method
2.1 Chinese Conversational Mathematical Intelligent Tutoring System
2.2 Experiment Topics
2.3 Data Analysis
3 Results
3.1 Learning Effectiveness
3.2 Learning Curve
3.3 Students' Learning Patterns
4 Conclusions
References
Evaluation Methods for an AI-Supported Learning Management System: Quantifying and Qualifying Added Values for Teaching and Learning
1 Trend Toward Intelligent Learning Environments
1.1 Terms and Definitions in the Context of Learning Environments
1.2 AI-Supported Functionalities in LMS and Their Possible Benefits
1.3 Miscellaneous AI-Based Trends in LMS/LXP
2 Evaluation of AI-Supported Functionalities in Learning Contexts
2.1 Qualitative Evaluation Methods
2.2 Quantitative Evaluation Based on Data Generated by the LMS Itself Using the Example of Recommender Systems
2.3 Usability and User Experience Evaluation
2.4 Using the Assessment of the Learner's Mental State for Evaluation
3 Summary and Best Practice
References
Impediments to AIS Adoption in the Philippines
1 Educational Outcomes in the Philippines
2 Hardware
3 Internet Access
4 Curriculum
5 Manpower
6 Philippine Education Under COVID-19
7 Discussion and Conclusion
References
Setting Goals in Adaptive Training: Can Learners Improve with a Moving Target?
1 Introduction
1.1 Implementing Instructional Strategies in Adaptive Training
1.2 Adaptive Training and Goal Setting
1.3 The Present Study
2 Method
2.1 Participants
2.2 Testbed
2.3 Procedure
3 Results
3.1 Stage 1
3.2 Stage 2: Gain Reports
3.3 Stage 2: Secure Reports
3.4 Final Report
3.5 Exploratory Analysis: Were Goals Met?
4 Discussion
4.1 A Comment on the Use of Directional Tests
4.2 Evaluating the Impact of Goal Setting
4.3 Feedback and Goal Setting
4.4 Limitations and Future Research
References
Teachers' Perspectives on the Adoption of an Adaptive Learning System Based on Multimodal Affect Recognition for Students with Learning Disabilities and Autism
1 Introduction
2 Methods
2.1 Design
2.2 Participants
2.3 Procedure
2.4 Analysis
3 Results
3.1 Theme 1: Transformative Potential
3.2 Theme 2: Ability to Impact Learning Outcomes
3.3 Theme 3: Potential Impact on Social Relationships
3.4 Theme 4: Ease of Use
3.5 Theme 5: Organisational Challenges
4 Discussion
References
Adaptive Modules on Prerequisite Chemistry Content Positively Impact Chiropractic Students' Proficiency in Biochemistry
1 Introduction
1.1 Chiropractic Education
1.2 Variation in Student Readiness
1.3 Performance Trends in Biochemistry at NWHSU
1.4 Adaptive Learning
1.5 Effect of Adaptive Units on Students' Performance in Biochemistry
2 Methods
2.1 Description of Adaptive Chemistry Units
2.2 Embedding the Adaptive Units into the Course
2.3 Studying the Effects of the Units
3 Results
3.1 Student Participation and Performance on Adaptive Units
3.2 Effect on Final Grade
3.3 Effect on Exam 1 Performance
3.4 Effect on Performance on the Final Exam
3.5 Are Students Deciding to Take It Slower?
4 Discussion
4.1 Student Participation and Performance in Adaptive Units
4.2 Effect on Final Grades
4.3 Effect on Two Summative Exam
4.4 Effect on Decision to Split
References
Using Adaptive Flashcards for Automotive Maintenance Training in the Wild
1 Introduction
1.1 The Benefits of Flashcards for Learning
1.2 Flashcard Sequencing
1.3 Flashcard Mastery
2 Current Research
2.1 Adaptive Flashcard System
3 Longitudinal Pilot Study
3.1 Comparison of Two Cohorts
3.2 Written Exam Performance Results
3.3 AT System Usage and Course Performance Results
3.4 Instructor and Student Reactions
4 Discussion and Future Research
References
Revealing Data Feature Differences Between System- and Learner-Initiated Self-regulated Learning Processes Within Hypermedia
1 Introduction
1.1 Theoretical Foundations
1.2 MetaTutor
1.3 Current Study
2 Methods
2.1 Participants and Conditions
2.2 Materials
2.3 Experimental Procedure
2.4 Coding and Scoring
3 Results
3.1 Are There Theoretically Explainable Principle Components for the Frequency System-Initiated Metacognitive and Cognitive Processes?
3.2 Are There Theoretically Explainable Principle Components for the Frequency of Learner-Initiated Metacognitive and Cognitive Processes?
3.3 Can the Principle Components for the Frequency of both System- and Learner-Initiated Metacognitive and Cognitive Processes Model Learning?
4 Discussion
References
Adaptation Strategies and Methods in AIS
Collecting 3A Data to Enhance HCI in AIS
1 Introduction
2 Related Literature
3 3A Data Collection
3.1 Individualized 3A Data
3.2 Collective 3A Data
3.3 Blended Human Tutor
4 Future Direction
5 Conclusion
References
Enhance Conversation-Based Tutoring System with Blended Human Tutor
1 Introduction
2 Related Literature
3 Experience API and Learning Record Store
4 Server Based Implementation
5 Serverless Implementation
6 WebRTC for Audio Visual Chat
7 Conclusion
References
Adapting to the Times: Examining Adaptive Instructional Strategies in Preferred and Non-preferred Class Types
1 Introduction
2 Methods
2.1 Participants
2.2 Interventions
2.3 Measures
2.4 Procedure
3 Results
3.1 Instructor Interview Results
3.2 Instructional Strategy and Preferred Class Type Effects on Workload, Stress, Engagement and Performance
3.3 Instructional Strategy and Preferred Class Type Effects on Factors that Influence Engagement
3.4 COVID-19 Learning Challenges
4 Discussion
4.1 Practical Implications
4.2 Study Limitations and Future Research
5 Conclusion
References
Alignment of Competency-Based Learning and Assessment to Adaptive Instructional Systems
1 Introduction
2 Competency-Based Learning Concepts
2.1 Principle Design Focus of Traditional Learning Versus Competency-Based Learning
2.2 Instructional Design and Courseware Development
2.3 Assessments in Competency-Based Learning
2.4 Role of Learning Analytics in Competency-Based Learning
3 Competency-Based Learning in the Industry
3.1 Application of Competency-Based Learning
3.2 Emirates Training Use Case
4 Computerized Adaptive Learning Systems
4.1 Design Elements
4.2 Example Computerized Instructional Adaptive System
4.3 Challenges to Applying Competency-Based Frameworks to Computerized Learning Systems
5 Summary
References
Developing an Adaptive Framework to Support Intelligence Analysis
1 Introduction
2 Hypothesis Generation
3 RAMPAGE Process Framework
4 Framework Adaptivity
4.1 Adaptive Timing
4.2 Adaptive Methods
4.3 Adaptive Complexity
5 Conclusion
References
Taxonomy of Physiologically Adaptive Systems and Design Framework
1 Introduction
2 Adaptive Techniques for Biocybernetic Systems
2.1 Control Theoretic Approaches
2.2 Machine Learning Approaches
3 Design Frameworks for Physiologically Adaptive Systems
4 Taxonomy for Adaptive Techniques in Biocybernetic Systems
5 Framework for Designing Physiologically Adaptive Systems
6 Discussion and Conclusion
6.1 Software Tools and New Immersive Media
6.2 Challenges in Machine Learning and Biocybernetic Adaptation
6.3 Feedback and Reactive Ambient
References
Intersectionality and Incremental Value: What Combination(s) of Student Attributes Lead to the Most Effective Adaptations of the Learning Environment?
1 Introduction
1.1 Potential Student Categories
2 Courses and Student Attributes
2.1 Course Selection
2.2 Student Attributes
3 Attribute Selection - C785 Biochemistry
3.1 Evaluation of Attributes
4 Cluster Creation and Performance - C785 Biochemistry
4.1 Clustering Method - C785
4.2 Clustering Results - Single Attribute Categories C785
4.3 Clustering Results - Two Attribute Categories C785
4.4 Clustering Results - Three Attribute Categories C785
5 Attribute Selection - C65 - Integrated Physical Science
5.1 Evaluation of Attributes
6 Cluster Creation and Performance - C165 Integrated Physical Science
6.1 Clustering Method
6.2 Differences by Student College in Outcome Variable - C165
6.3 Differences by Student College in Population Discrimination - C165
7 Discussion of Findings - Recommendations for Adaptive Systems
7.1 What Attributes Matter to Student Success?
7.2 What Should Designers of Adaptive Learning Resources Do with This?
7.3 What Should Instructors and Students Do with This?
8 Next Steps and Research Questions
8.1 Exploratory Work
References
The Adaptive Features of an Intelligent Tutoring System for Adult Literacy
1 Introduction
1.1 Adult Learners
1.2 AutoTutor
2 AT-ARC and Its Theory
2.1 AT-ARC Lessons
2.2 Theoretical Model of Comprehension
3 Adaptive Features of AT-ARC
3.1 Learning Material Selection
3.2 Adaptive Branching
3.3 Trialogues
3.4 Interface
4 Final Thoughts
References
Considerations Towards Culturally-Adaptive Instructional Systems
1 Introduction
2 Complexities of Culture
3 Culture and Education
3.1 Culture in Instructional Systems
4 Determining Features with Cultural Biases
4.1 Universal Design for Learning (UDL)
4.2 Cultural Artefacts in Education
5 Discussion
6 Conclusion and Future Work
References
Applying Adaptive Intelligent Tutoring Techniques to Physical Fitness Training Programs
1 Introduction
2 Methods
2.1 Participants and Attrition
2.2 Physical Training Regimen
2.3 Data Analysis
3 Results
3.1 Passing the Initial Strength Test
3.2 Statistical Analysis
3.3 Curve Fitting
4 Discussion
References
Correction to: Teachers' Perspectives on the Adoption of an Adaptive Learning System Based on Multimodal Affect Recognition for Students with Learning Disabilities and Autism
Correction to: Chapter "Teachers' Perspectives on the Adoption of an Adaptive Learning System Based on Multimodal Affect Recognition for Students with Learning Disabilities and Autism" in: R. A. Sottilare and J. Schwarz (Eds.): Adaptive Instructional Systems, LNCS 12792, https://doi.org/10.1007/978-3-030-77857-6_31
Correction to: Personalized Mastery Learning Ecosystems: Using Bloom's Four Objects of Change to Drive Learning in Adaptive Instructional Systems
Correction to: Chapter "Personalized Mastery Learning Ecosystems: Using Bloom's Four Objects of Change to Drive Learning in Adaptive Instructional Systems" in: R. A. Sottilare and J. Schwarz (Eds.): Adaptive Instructional Systems, LNCS 12792, https://doi.org/10.1007/978-3-030-77857-6_3
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Adaptive Instructional Systems. Design and Evaluation

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