1st Edition
Toward Trustworthy Adaptive Learning Explainable Learner Models
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
Biography
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.
