Atnaujinkite slapukų nuostatas

El. knyga: Toward Trustworthy Adaptive Learning: Explainable Learner Models

(East China Normal University, China)
Kitos knygos pagal šią temą:
Toward Trustworthy Adaptive Learning: Explainable Learner ModelsDidesnis paveikslėlis

DRM apribojimai

  • Kopijuoti:

    neleidžiama

  • Spausdinti:

    neleidžiama

  • El. knygos naudojimas:

    Skaitmeninių teisių valdymas (DRM)
    Leidykla pateikė šią knygą šifruota forma, o tai reiškia, kad norint ją atrakinti ir perskaityti reikia įdiegti nemokamą programinę įrangą. Norint skaityti šią el. knygą, turite susikurti Adobe ID . Daugiau informacijos  čia. El. knygą galima atsisiųsti į 6 įrenginius (vienas vartotojas su tuo pačiu Adobe ID).

    Reikalinga programinė įranga
    Norint skaityti šią el. knygą mobiliajame įrenginyje (telefone ar planšetiniame kompiuteryje), turite įdiegti šią nemokamą programėlę: PocketBook Reader (iOS / Android)

    Norint skaityti šią el. knygą asmeniniame arba „Mac“ kompiuteryje, Jums reikalinga  Adobe Digital Editions “ (tai nemokama programa, specialiai sukurta el. knygoms. Tai nėra tas pats, kas „Adobe Reader“, kurią tikriausiai jau turite savo kompiuteryje.)

    Negalite skaityti šios el. knygos naudodami „Amazon Kindle“.

"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 canenhance 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 illustratehow 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"--

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
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.
Daugiau apie elektronines knygas Daugiau apie elektronines knygas
Pastovi nuoroda: https://www.kriso.lt/db/97810403122616e.html
Raktažodžiai: