Perrotta, C orcid.org/0000-0003-3572-0844 and Williamson, B (2018) The social life of Learning Analytics: cluster analysis and the ‘performance’ of algorithmic education. Learning, Media & Technology, 43 (1). pp. 3-16. ISSN 1743-9884
Abstract
This paper argues that methods used for the classification and measurement of online education are not neutral and objective, but involved in the creation of the educational realities they claim to measure. In particular, the paper draws on material semiotics to examine cluster analysis as a ‘performative device’ that, to a significant extent, creates the educational entities it claims to objectively represent through the emerging body of knowledge of Learning Analytics (LA). It also offers a more critical and political reading of the algorithmic assemblages of LA, of which cluster analysis is a part. Our argument is that if we want to understand how algorithmic processes and techniques like cluster analysis function as performative devices, then we need methodological sensibilities that consider critically both their political dimensions and their technical-mathematical mechanisms. The implications for critical research in educational technology are discussed.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Algorithms; Cluster Analysis; Learning Analytics; Methods; Performativity |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Education, Social Sciences and Law (Leeds) > School of Education (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 May 2016 09:20 |
Last Modified: | 20 Apr 2018 04:19 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/17439884.2016.1182927 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:99854 |