Toward Trustworthy Adaptive Learning
portes grátis
Toward Trustworthy Adaptive Learning
Explainable Learner Models
Jiang, Bo
Taylor & Francis Ltd
03/2025
217
Dura
Inglês
9781032954950
15 a 20 dias
Descrição não disponível.
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Adaptive-learning-systems;Educational-technology;Educational-data-mining;Explainable-learner-models;Interpretable-models;Temporal-Fuzzy-Cognitive-Map;Intelligent-tutoring-systems
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
