Computational Learning Theories: Models for Artificial Intelligence Promoting Learning Processes (Advances in Analytics for Learning and Teaching)

✍ Scribed by David C. Gibson, Dirk Ifenthaler

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 164
• Edition: 2024
• Category: Library

✦ Synopsis

This book shows how artificial intelligence grounded in learning theories can promote individual learning, team productivity and multidisciplinary knowledge-building. It advances the learning sciences by integrating learning theory with computational biology and complexity, offering an updated mechanism of learning, which integrates previous theories, provides a basis for scaling from individuals to societies, and unifies models of psychology, sociology and cultural studies.

The book provides a road map for the development of AI that addresses the central problems of learning theory in the age of artificial intelligence including:

• optimizing human-machine collaboration
• promoting individual learning
• balancing personalization with privacy
• dealing with biases and promoting fairness
• explaining decisions and recommendations to build trust and accountability
• continuously balancing and adapting to individual, team and organizational goals
• generating and generalizing knowledge across fields and domains

The book will be of interest to educational professionals, researchers, and developers of educational technology that utilize artificial intelligence.

✦ Table of Contents

Preface
Organization of the Book
Key Features of the Book
Contents
About the Authors
Chapter 1: Why ‘Computational’ Learning Theories?
1.1 Introduction
1.2 Overlapping and Conflicting Ideas in Learning Theories
1.3 Pros and Cons of a Computational Framework
1.3.1 The Partiality of Description
1.3.2 Tenets of Evolution and Complexity
1.4 Computational Models of Learning
1.4.1 A Spreadsheet Model of Computational Modeling of Complexity
1.5 Limitations and Conclusions
1.6 Moving Forward
References
Chapter 2: AI and Learning Processes
2.1 Introduction
2.2 Learning Processes as the Key Leverage Point
2.3 Introducing the New Framework
2.4 Potential Pitfalls of AI Interventions in Education
2.5 Limitations and Conclusions
2.6 Moving Forward
References
Chapter 3: A Complex Hierarchical Framework of Learning
3.1 Introduction
3.2 Learning Processes at the Micro, Meso, and Macro Levels
3.2.1 Individual Learning—The Micro Level
3.2.2 Social Learning—The Meso Level
3.2.3 Sociocultural Evolution—The Macro Level
3.3 Dynamic Causal Network Concepts in the Trilevel Model
3.4 Limitations and Conclusions
3.5 Moving Forward
References
Chapter 4: Piaget and the Ontogeny of Intelligence
4.1 Introduction
4.2 Cognitive Equilibration Theory
4.2.1 Disequilibration ‘Notices’
4.2.2 Assimilation ‘Remembers and Interprets’
4.2.3 Accommodation ‘Creates Adaptations’
4.2.4 Equilibration ‘Satisfies and Maintains’
4.3 Limitations and Conclusions
4.4 Moving Forward
References
Chapter 5: Keller and the ARCS Model of Motivation
5.1 Introduction
5.2 Models of Causation
5.3 Motivation and the Causal Learning Cycle
5.4 Limitations and Conclusions
5.5 Moving Forward
References
Chapter 6: Complexity Theory and Learning
6.1 Introduction
6.2 Ilya Prigogine
6.3 Stuart Kauffman
6.4 Physics, Information, and Autocatalysis
6.4.1 Physical Control Systems
6.4.2 Smart Systems
6.4.3 Learning Systems
6.5 Complexity and Educational Theory
6.6 Limitations and Conclusions
6.7 Moving Forward
References
Chapter 7: AI Roles for Enhancing Individual Learning
7.1 Introduction
7.2 Models of Self-Regulated Learning
7.3 AI as a Partner
7.4 Elaborating the AI Roles of Individual Learning Processes
7.4.1 Personalized Recommendations
7.4.2 Learning Analytics
7.4.3 Pedagogical Interventions
7.4.4 Formative and Summative Feedback
7.5 Evidence and Indicators from Historical Learning Theories
7.6 Limitations and Conclusions
7.7 Moving Forward
References
Chapter 8: Informal Social Learning
8.1 Introduction
8.2 Dewey’s Social Pragmaticism
8.3 Relating Dewey’s Social Learning Ideas to the Micro Level
8.3.1 Disequilibration
8.3.2 Assimilation
8.3.3 Accommodation
8.3.4 Equilibration
8.4 Implications of Dewey for Social Learning AI
8.5 Limitations and Conclusions
8.6 Moving Forward
References
Chapter 9: How People Learn
9.1 Introduction
9.1.1 Knowledge Is Dynamic and Collaboratively Determined
9.1.2 Community Experts Own the Knowledge and Learning Processes
9.1.3 Assessment Feedback Develops Community Expertise
9.1.4 Learners Are the Creative Force of Knowledge Communities
9.2 Homologous Functions of the Learning Cycle at the Meso Level
9.3 Limitations and Conclusions
9.4 Moving Forward
References
Chapter 10: AI Assisting Individuals as Team Members
10.1 Introduction
10.1.1 Learner: Matching Individual Capabilities to Task Requirements
10.1.1.1 Skill Mapping
10.1.1.2 Task-Skill Allocation
10.1.2 Community: Fulfilling Collaboration Roles
10.1.2.1 Collaboration Facilitation
10.1.2.2 Task-Specific Collaboration Support
10.1.3 Assessment: Maintaining Cognitive Presence and Team Focus
10.1.3.1 Cognitive Monitoring
10.1.3.2 Adaptive Support Systems
10.1.4 Knowledge: Fulfilling Problem-Solving and Knowledge-Building Roles
10.1.4.1 Knowledge Repository Access
10.1.4.2 AI-Supported Ideation
10.2 Limitations and Conclusions
10.3 Moving Forward
References
Chapter 11: AI Roles for the Team or Organization
11.1 Introduction
11.2 The HPL Framework of a Learning Organization
11.2.1 Building and Maintaining Individual Learner Models
11.2.1.1 Personalized Learning Paths
11.2.1.2 Dynamic Model Updates
11.2.2 Managing Human Shaping of Knowledge Fields
11.2.2.1 Content Curation
11.2.2.2 Knowledge Evolution Monitoring
11.2.3 Providing Evidence-Based Feedback to Members
11.2.3.1 Performance Analytics
11.2.3.2 Behavioral Feedback
11.2.4 Finding New Links and Research Lines
11.2.4.1 Cross-Domain Connections
11.2.4.2 Research Recommendation Systems
11.3 From Teams and Organizations to Global Enterprises
11.4 Limitations and Conclusions
11.5 Moving Forward
References
Chapter 12: A Network Theory of Culture
12.1 Introduction
12.2 Cultural Mediation
12.3 Relating the Macro to Meso Levels
12.4 Limitations and Conclusions
12.5 Moving Forward
References
Chapter 13: AI Roles in Cultural Learning
13.1 Introduction
13.2 AI as a Mediator and Initiator of Cultural Shifts
13.3 Goals for AI Assisting Cultural Learning
13.3.1 Co-creating Learning Artifacts
13.3.2 Enhancing Interdisciplinary Goal Clarity and Planning
13.3.3 Balancing Agency Issues from the Micro and Meso Levels
13.3.4 Facilitating Cross-Cutting Communities and Expert Integration
13.3.5 Supporting Community Roles
13.3.6 Evolution of Algorithms
13.4 Limitations and Conclusions
13.5 Moving Forward
References
Chapter 14: Open Questions
References