Adoption of data analytics in higher education learning an teaching




Publisher: Ifenthaler, Dirk ; Gibson, David
DOI: https://doi.org/10.1007/978-3-030-47392-1
URL: https://www.springer.com/gp/book/9783030473914
Document Type: Book
Year of publication: 2020
Place of publication: Cham
Publishing house: Springer
ISBN: 978-3-030-47391-4 , 978-3-030-47392-1
Publication language: English
Institution: Business School > Wirtschaftspädagogik V (Ifenthaler 2015-)
Subject: 370 Education
Abstract: 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.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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