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Appears in Collections:Faculty of Social Sciences Journal Articles
Peer Review Status: Refereed
Title: Moulding student emotions through computational psychology: affective learning technologies and algorithmic governance
Authors: Williamson, Ben
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Keywords: affective computing
big data
computational psychology
social-emotional learning
Issue Date: 2017
Citation: Williamson B (2017) Moulding student emotions through computational psychology: affective learning technologies and algorithmic governance, Educational Media International, 54 (4), pp. 267-288.
Abstract: Recently psychology has begun to amalgamate with computer science approaches to big data analysis as a new field of ‘computational psychology’ or ‘psycho-informatics,’ as well as with new ‘psycho-policy’ approaches associated with behaviour change science, in ways that propose new ways of measuring, administering and managing individuals and populations. In particular, ‘social-emotional learning’ has become a new focus within education. Supporters of social-emotional learning foresee technical systems being employed to quantify and govern learners’ affective lives, and to modify their behaviours in the direction of ‘positive’ feelings. In this article I identify the core aspirations of computational psychology in education, along with the technical systems it proposes to enact its vision, and argue that a new form of ‘psycho-informatic power’ is emerging as a source of authority and control over education.
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