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.
Author: | Sima Caspari-SadeghiORCiD |
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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): | Creative Commons - CC BY - Namensnennung 4.0 International |