Adoption of data analytics in higher education learning and teaching /

Adoption of data analytics in higher education learning and teaching /

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Bibliographic Details
Imprint:	Cham, Switzerland : Springer, [2020]
Description:	1 online resource
Language:	English
Series:	Advances in Analytics for Learning and Teaching Ser. Advances in Analytics for Learning and Teaching.
Subject:	Education, Higher -- Research -- United States -- Data processing. Education, Higher -- Research -- United States -- Statistical methods. Educational statistics. Education, Higher -- Research -- Data processing. Educational statistics. United States. Electronic books.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/12607116

Hidden Bibliographic Details
Other authors / contributors:	Ifenthaler, Dirk, editor. Gibson, David, editor.
ISBN:	9783030473921 3030473929 3030473910 9783030473914
Digital file characteristics:	text file PDF
Notes:	Description based on online resource; title from digital title page (viewed on September 03, 2020).
Summary:	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.
Other form:	Print version: 3030473910 9783030473914
Standard no.:	10.1007/978-3-030-47

Description
Summary:	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.
Physical Description:	1 online resource
ISBN:	9783030473921 3030473929 3030473910 9783030473914