Toward Trustworthy Adaptive Learning

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 ContentsPreface AuthorsContributorsSection I. Explainable Learner Models: An Overview1. Trustworthy AI for Adaptive Learning2. Explainable Learner Models: Concepts, Classifications, and Datasets3. Construction and Interpretation of Explainable Models: A Case Study on BKTSection II. Research on Ante-hoc Explainability Learner Models4. Interpretable Cognitive State Prediction via Temporal Fuzzy Cognitive Map5. Improving the performance and explainability of knowledge tracing via Markov blanket6. Knowledge Tracing within Single Programming Practice Using Problem-Solving Process DataSection III. Research on Post-hoc Explainability Learner Models7. Understanding the relationship between computational thinking and computational participation8. Understanding students’ backtracking behaviour in digital textbooks: a data-driven perspectiveSection IV. Toward Trustworthy Adaptive Learning9. Frameworks for Explainable Learner Models10. Frameworks for Trustworthy AI for Adaptive LearningIndex