| ISBN13: | 9781032954950 |
| ISBN10: | 1032954957 |
| Kötéstípus: | Keménykötés |
| Terjedelem: | 234 oldal |
| Méret: | 234x156 mm |
| Nyelv: | angol |
| Illusztrációk: | 60 Illustrations, black & white; 60 Halftones, black & white; 27 Tables, black & white |
| 700 |
Toward Trustworthy Adaptive Learning
GBP 145.00
Kattintson ide a feliratkozáshoz
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.
This book offers an in-depth exploration of explainable learner models, presenting theoretical foundations and practical applications in the context of educational AI. It aims to provide readers with a comprehensive understanding of how these models can enhance adaptive learning systems.
Chapters cover a wide range of topics, including the development and optimization of explainable learner models, the integration of these models into adaptive learning systems, and their implications for educational equity. It also discusses the latest advancements in AI explainability techniques, such as pre-hoc and post-hoc explainability, and their application in intelligent tutoring systems. Lastly, the book provides practical examples and case studies to illustrate how explainable learner models can be implemented in real-world educational settings.
This book is an essential resource for researchers, educators, and practitioners interested in the intersection of AI and education. It offers valuable insights for those looking to integrate explainable AI into their educational practices, as well as for policymakers focused on promoting equitable and transparent learning environments.
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
