|Appears in Collections:||Faculty of Social Sciences Journal Articles|
|Peer Review Status:||Refereed|
|Title:||The social life of Learning Analytics: cluster analysis and the ‘performance’ of algorithmic education (Forthcoming/Available Online)|
|Citation:||Perrotta C & Williamson B (2016) The social life of Learning Analytics: cluster analysis and the ‘performance’ of algorithmic education (Forthcoming/Available Online), Learning, Media and Technology.|
|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.|
|Rights:||© 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.|
|The social life of Learning Analytics cluster analysis and the performance of algorithmic education.pdf||1.28 MB||Adobe PDF||View/Open|
This item is protected by original copyright
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
If you believe that any material held in STORRE infringes copyright, please contact firstname.lastname@example.org providing details and we will remove the Work from public display in STORRE and investigate your claim.