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    Toward Trustworthy Adaptive Learning: Explainable Learner Models

    Toward Trustworthy Adaptive Learning by Jiang, Bo;

    Explainable Learner Models

    Series: Assessment of Educational Technology;

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    21 757 Ft

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    Availability

    Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
    Not in stock at Prospero.

    Why don't you give exact delivery time?

    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Product details:

    • Edition number 1
    • Publisher Routledge
    • Date of Publication 31 March 2025

    • ISBN 9781032954943
    • Binding Paperback
    • No. of pages234 pages
    • Size 234x156 mm
    • Weight 430 g
    • Language English
    • Illustrations 60 Illustrations, black & white; 60 Halftones, black & white; 27 Tables, black & white
    • 700

    Categories

    Short description:

    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.

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    Long description:

    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.

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    Table of Contents:

    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

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