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Adoption of data analytics in higher...

Gibson, David.

Adoption of data analytics in higher education learning and teaching

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Adoption of data analytics in higher education learning and teachingedited by Dirk Ifenthaler, David Gibson.
其他作者:	Ifenthaler, Dirk.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xxxviii, 434 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Education, HigherResearchUnited States
電子資源:	https://doi.org/10.1007/978-3-030-47392-1
ISBN:	9783030473921$q(electronic bk.)

Adoption of data analytics in higher education learning and teaching
Adoption of data analytics in higher education learning and teaching[electronic resource] /edited by Dirk Ifenthaler, David Gibson. - Cham :Springer International Publishing :2020. - xxxviii, 434 p. :ill., digital ;24 cm. - Advances in analytics for learning and teaching,2662-2122. - Advances in analytics for learning and teaching..

Part I. Theoretical Foundations and Frameworks -- Part II. Technological Infrastructure and Staff Requirements -- Part III. Institutional Governance and Policy Implementation -- Part IV. Case Studies.

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.

ISBN: 9783030473921$q(electronic bk.)

Standard No.: 10.1007/978-3-030-47392-1doiSubjects--Topical Terms:

876086
Education, Higher
--Research--United States

LC Class. No.: LB2395.7 / .A46 2020

Dewey Class. No.: 378.007

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