Adoption of Data Analytics in Higher Education Learning and Teaching. 1st ed. 2020.

Sonst. Personen	Ifenthaler, Dirk (Hrsg.); Gibson, David (Hrsg.)
Titel	Adoption of Data Analytics in Higher Education Learning and Teaching. 1st ed. 2020.
Quelle	Cham: Springer International Publishing (2020), XXXVIII, 434 S. PDF als Volltext Link als defekt meldenVerfügbarkeit
Reihe	Advances in Analytics for Learning and Teaching
Beigaben	Illustrationen 104; farbige Illustrationen 74
Sprache	englisch
Dokumenttyp	online; gedruckt; Monographie
ISBN	978-3-030-47391-4; 978-3-030-47392-1; 978-3-030-47393-8; 978-3-030-47394-5
DOI	10.1007/978-3-030-47392-1
Schlagwörter	Lernen; Unterrichtsprozess; Unterrichtsmedien; Unterrichtstechnologie; Hochschulbildung; Hochschulsystem
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. Contents: Part I. Theoretical Foundations and Frameworks -- Part II. Technological Infrastructure and Staff Requirements -- Part III. Institutional Governance and Policy Implementation -- Part IV. Case Studies.
Erfasst von	Universitätsbibliothek der Humboldt-Universität zu Berlin
Update	2022/2