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Learning assessment in the age of big data: Learning analytics in higher education

  • Data-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and entertainment. During the COVID-19 pandemic, huge amounts of educational data and new types of evidence were generated through various online platforms, digital tools, and communication applications. Meanwhile, it is acknowledged that educa-tion lacks computational infrastructure and human capacity to fully exploit the potential of big data. This paper explores the use of Learning Analytics (LA) in higher education for measurement purposes. Four main LA functions in the assessment are outlined: (a) monitoring and analysis, (b) automated feedback, (c) prediction, prevention, and intervention, and (d) new forms of assessment. The paper con-cludes by discussing the challenges of adopting and upscaling LA as well as the implications for instructors in higher education.

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Metadaten
Author:Sima Caspari-SadeghiORCiD
URN:urn:nbn:de:bvb:739-opus4-12236
DOI:https://doi.org/0.1080/2331186X.2022.2162697
Parent Title (English):Cogent Education
Publisher:Taylor & Francis
Document Type:Article
Language:English
Year of Completion:2022
Year of Completion:2022
Date of first Publication:2022/12/31
Publishing Institution:Universität Passau
Release Date:2023/03/27
Tag:Big data; learning analytics; technology-enhanced assessment
Volume:2023
Issue:Volume 110, issue 1
Page Number:11 Seiten
Institutes:Philosophische Fakultät
Dewey Decimal Classification:3 Sozialwissenschaften / 37 Bildung und Erziehung / 370 Bildung und Erziehung
open_access (DINI-Set):open_access
Funding Acknowledgement:Gefördert durch den Open-Access-Publikationsfonds der Universitätsbibliothek Passau.
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International