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Appears in Collections:Faculty of Social Sciences Journal Articles
Peer Review Status: Refereed
Title: Learning analytics: challenges and limitations
Author(s): Wilson, Anna
Watson, Cate
Thompson, Terrie Lynn
Drew, Valerie
Doyle, Sarah
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Keywords: Learning analytics
Big Data
professional learning
Issue Date: 2017
Citation: Wilson A, Watson C, Thompson TL, Drew V & Doyle S (2017) Learning analytics: challenges and limitations. Teaching in Higher Education, 22 (8), pp. 991-1007.
Abstract: Learning analytic implementations are increasingly being included in learning management systems in higher education. We lay out some concerns with the way learning analytics – both data and algorithms – are often presented within an unproblematized Big Data discourse. We describe some potential problems with the often implicit assumptions about learning and learners – and indeed the tendency not to theorize learning explicitly – that underpin such implementations. Finally, we describe an attempt to devise our own analytics, grounded in a sociomaterial conception of learning. We use the data obtained to suggest that the relationships between learning and the digital traces left by participants in online learning are far from trivial, and that any analytics that relies on these as proxies for learning tends towards a behaviorist evaluation of learning processes.
DOI Link: 10.1080/13562517.2017.1332026
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. This is an Accepted Manuscript of an article published by Taylor & Francis Group in Teaching in Higher Education on 24 May 2017, available online:

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