Toward Trustworthy Adaptive Learning

This book offers an in-depth exploration of explainable learner models, presenting theoretical foundations and practical applications in the context of educational AI. A valuable resource for researchers and educators, as well as for policymakers focused on promoting equitable and transparent learning environments.

Bo Jiang is an associate professor at East China Normal University, China. His research interests include intelligent tutoring technologies, computational thinking education, and AI education. He holds academic positions as an executive committee member of the Asia-Pacific Society for Computers in Education (APSCE) and a youth committee member of the Chinese Association for Artificial Intelligence.

Table of Contents Preface Authors Contributors Section I. Explainable Learner Models: An Overview 1. Trustworthy AI for Adaptive Learning 2. Explainable Learner Models: Concepts, Classifications, and Datasets 3. Construction and Interpretation of Explainable Models: A Case Study on BKT Section II. Research on Ante-hoc Explainability Learner Models 4. Interpretable Cognitive State Prediction via Temporal Fuzzy Cognitive Map 5. Improving the performance and explainability of knowledge tracing via Markov blanket 6. Knowledge Tracing within Single Programming Practice Using Problem-Solving Process Data Section III. Research on Post-hoc Explainability Learner Models 7. Understanding the relationship between computational thinking and computational participation 8. Understanding students' backtracking behaviour in digital textbooks: a data-driven perspective Section IV. Toward Trustworthy Adaptive Learning 9. Frameworks for Explainable Learner Models 10. Frameworks for Trustworthy AI for Adaptive Learning Index