Adoption of Data Analytics in Higher Education Learning and Teaching
Editors: Ifenthaler, Dirk, Gibson, David (Eds.)
Free Preview- Provides insights into how higher education institutions adopt learning analytics and data mining studies
- Contributions from distinguished international researchers
- Considers theoretical perspectives, innovative technologies,implementation, and assessment strategies for learning analytics in higher education
- Includes case studies showing innovative approaches for learning analytics in higher education
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- About this book
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The book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all. Currently, higher education institutions and involved stakeholders can derive multiple benefits from educational data mining and learning analytics by using different data analytics strategies to produce summative, real-time, and predictive or prescriptive insights and recommendations. Educational data mining refers to the process of extracting useful information out of a large collection of complex educational datasets while learning analytics emphasizes insights and responses to real-time learning processes based on educational information from digital learning environments, administrative systems, and social platforms.
This volume provides insight into the emerging paradigms, frameworks, methods and processes of managing change to better facilitate organizational transformation toward implementation of educational data mining and learning analytics. It features current research exploring the (a) theoretical foundation and empirical evidence of the adoption of learning analytics, (b) technological infrastructure and staff capabilities required, as well as (c) case studies that describe current practices and experiences in the use of data analytics in higher education.
- Table of contents (21 chapters)
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Adoption of Learning Analytics
Pages 3-20
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The Politics of Learning Analytics
Pages 21-38
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A Framework to Support Interdisciplinary Engagement with Learning Analytics
Pages 39-52
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The Framework of Learning Analytics for Prevention, Intervention, and Postvention in E-Learning Environments
Pages 53-69
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The LAVA Model: Learning Analytics Meets Visual Analytics
Pages 71-93
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Table of contents (21 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Adoption of Data Analytics in Higher Education Learning and Teaching
- Editors
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- Dirk Ifenthaler
- David Gibson
- Series Title
- Advances in Analytics for Learning and Teaching
- Copyright
- 2020
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer Nature Switzerland AG
- eBook ISBN
- 978-3-030-47392-1
- DOI
- 10.1007/978-3-030-47392-1
- Hardcover ISBN
- 978-3-030-47391-4
- Series ISSN
- 2662-2122
- Edition Number
- 1
- Number of Pages
- XXXVIII, 434
- Number of Illustrations
- 30 b/w illustrations, 74 illustrations in colour
- Topics
