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Explainable Artificial Intelligence and Interpretable Machine Learning: Bridging Data Science and Educational Inquiry de Myint Swe Khine

Explainable Artificial Intelligence and Interpretable Machine Learning: Bridging Data Science and Educational Inquiry

Editat de Myint Swe Khine
en Limba Engleză Hardback – 11 feb 2027
In today’s dynamic world of modern learning, Explainable AI (XAI) and Interpretable Machine Learning (IML) are transforming the way knowledge is delivered, assessed, and optimized. This book serves as a definitive resource that equips readers with the insights and practical strategies needed to harness these groundbreaking technologies, thus advancing transparency, efficiency, and innovation in teaching, learning, and academic research.
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Specificații

ISBN-13: 9781041217404
ISBN-10: 1041217404
Pagini: 266
Ilustrații: 88
Dimensiuni: 156 x 234 mm
Ediția:1
Editura: CRC Press
Colecția CRC Press

Public țintă

Academic, Postgraduate, Professional Practice & Development, and Undergraduate Advanced

Cuprins

Preface. Part I: Introduction. 1. Explaining Explainability in Education: Integrating Data Science, Interpretation, and Human Understanding. Part II: Explainable AI Methods, Models, and Technical Foundations in Education. 2. Concept-Based Methods. 3. That Makes Sense: XAI Techniques to Interpret Machine Learning (ML) Model Predictions Using an Education Data Case Study. 4. Explainable Multimodal Learning Analytics Transformer-Era Methods for Transparent Student Success and Well-Being. 5. From ‘Why Did They Leave?’ to ‘How Could We Keep Them?’ Counterfactual Explainable AI for Student Retention. 6. Conformal Prediction for Educational Data: Mondrian Subgroup Analysis and Uncertainty Quantification. 7. Behavioral Analysis of Student Engagement in Virtual Classrooms: An Explainable AI Approach with SHAP and LIME. 8. Predicting Dropout with Explainable AI Using Progressively Available Student Data. Part III: Explainable AI in Educational and Social Contexts: Ethics, Discourse, Assessment, and Talent Development. 9. Illuminating the Black Box: XAI and Talent Development. 10. Explainable AI for Large Language Models in Automated Educational Assessment: Needs, Challenges, and Opportunities. 11. Beyond Representation: Using Explainable Artificial Intelligence (XAI) and Critical Discourse Analysis to Detect Absence in Texts. Index.

Notă biografică

Professor Myint Swe Khine holds Master's degrees from the University of Southern California, USA, and the University of Surrey, UK, as well as a Doctor of Education from Curtin University, Australia. He has worked at the National Institute of Education at Nanyang Technological University, Singapore, and was a Professor at Emirates College for Advanced Education in the United Arab Emirates. He currently teaches at the School of Education, Curtin University, Australia. Dr. Khine is also the Editor-in-Chief of the Journal of Science of Learning and Innovations. Throughout his career, he has published over 40 edited books.  The most recent volumes include Future of Learning with Large Language Models: Applications and Research in Education, published by CRC Press.

Descriere

This book serves as a definitive resource that equips readers with the insights and practical strategies needed to harness these groundbreaking technologies, thus advancing transparency, efficiency, and innovation in teaching, learning, and academic research.