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Critical Data Studies, Abstraction & Learning Analytics
Author(s) -
Simon Buckingham Shum
Publication year - 2019
Publication title -
journal of learning analytics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.084
H-Index - 7
ISSN - 1929-7750
DOI - 10.18608/jla.2019.63.2
Subject(s) - learning analytics , analytics , abstraction , data science , computer science , epistemology , sociology , cognitive science , psychology , philosophy
This editorial introduces a special section of the Journal of Learning Analytics, for which Neil Selwyn’s keynote address to LAK ’18 has been written up as an article, “What’s the problem with learning analytics?” His claims and arguments are engaged in commentaries from Alfred Essa, Rebecca Ferguson, Paul Prinsloo, and Carolyn Rosé, who provide diverse perspectives on Selwyn’s proposals and arguments, from applause to refutation. Reflecting on the debate, I note some of the tensions to be resolved for learning analytics and social science critiques to engage productively, observing that central to the debate is how we understand the role of abstraction in the analysis of data about teaching and learning, and hence the opportunities and risks this entails.  

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