Overview
- Presents recent machine learning paradigms and advances in learning analytics
- Provides concise coverage from the vantage point of a newcomer, but will also appeal to experts/researchers in learning analytics
- Features an extended list of bibliographic references that completely covers the relevant literature
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 158)
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Table of contents (11 chapters)
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Front Matter
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Learning Analytics with the Purpose to Measure Student Engagement, to Quantify the Learning Experience and to Facilitate Self-Regulation
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Front Matter
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Learning Analytics to Predict Student Performance
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Front Matter
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Learning Analytics Incorporated in Tools for Building Learning Materials and Educational Courses
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Front Matter
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Learning Analytics as Tools to Support Learners and Educators in Synchronous and Asynchronous e-Learning
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Front Matter
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Keywords
About this book
This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including:
• Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation;
• Using learning analytics to predict student performance;
• Using learning analytics to create learning materials and educational courses; and
• Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning.
The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.
Reviews
Editors and Affiliations
About the editors
Bibliographic Information
Book Title: Machine Learning Paradigms
Book Subtitle: Advances in Learning Analytics
Editors: Maria Virvou, Efthimios Alepis, George A. Tsihrintzis, Lakhmi C. Jain
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-030-13743-4
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-13742-7Published: 26 March 2019
eBook ISBN: 978-3-030-13743-4Published: 16 March 2019
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XVI, 223
Number of Illustrations: 31 b/w illustrations, 29 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Learning & Instruction, Data Mining and Knowledge Discovery