Toward Trustworthy Adaptive Learning: Explainable Learner Models: Assessment of Educational Technology
Autor Bo Jiang, Yuang Weien Limba Engleză Hardback – 20 mar 2025
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.
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 360.08 lei 22-36 zile | +19.52 lei 5-11 zile |
| Taylor & Francis – 20 mar 2025 | 360.08 lei 22-36 zile | +19.52 lei 5-11 zile |
| Hardback (1) | 1015.58 lei 43-57 zile | |
| Taylor & Francis – 20 mar 2025 | 1015.58 lei 43-57 zile |
Preț: 1015.58 lei
Preț vechi: 1238.52 lei
-18% Nou
Puncte Express: 1523
Preț estimativ în valută:
179.71€ • 210.73$ • 157.82£
179.71€ • 210.73$ • 157.82£
Carte tipărită la comandă
Livrare economică 16 februarie-02 martie
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9781032954950
ISBN-10: 1032954957
Pagini: 264
Ilustrații: 120
Dimensiuni: 156 x 234 mm
Greutate: 0.51 kg
Ediția:1
Editura: Taylor & Francis
Colecția Routledge
Seria Assessment of Educational Technology
Locul publicării:Oxford, United Kingdom
ISBN-10: 1032954957
Pagini: 264
Ilustrații: 120
Dimensiuni: 156 x 234 mm
Greutate: 0.51 kg
Ediția:1
Editura: Taylor & Francis
Colecția Routledge
Seria Assessment of Educational Technology
Locul publicării:Oxford, United Kingdom
Public țintă
Professional Practice & Development, Professional Reference, and Undergraduate AdvancedCuprins
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
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
Notă biografică
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.
Yuang Wei is a PhD student at East China Normal University, specializing in intelligent education and Explainable AI. His research focuses on adaptive learning systems and enhancing educational equity. His work has been published in several peer-reviewed journals.
Yuang Wei is a PhD student at East China Normal University, specializing in intelligent education and Explainable AI. His research focuses on adaptive learning systems and enhancing educational equity. His work has been published in several peer-reviewed journals.
Descriere
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.