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Anticipating Education: Governing Habits, Memories and Policy-Futures

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Title: Anticipating Education: Governing Habits, Memories and Policy-Futures
Language: English
Authors: Webb, P. Taylor (ORCID 0000-0003-1207-4333), Sellar, Sam (ORCID 0000-0002-2840-5021), Gulson, Kalervo N.
Source: Learning, Media and Technology. 2020 45(3):284-297.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 14
Publication Date: 2020
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Data Use, Artificial Intelligence, Educational Trends, Futures (of Society), Time, Governance, Educational Policy
DOI: 10.1080/17439884.2020.1686015
ISSN: 1743-9884
Abstract: The use of data to govern education is increasingly supported by the use of knowledge-based technologies, including algorithms, artificial intelligence (AI), and tracking technologies [Fenwick, T., E. Mangez, and J. Ozga. 2014. "Governing Knowledge: Comparison, Knowledge-Based Technologies and Expertise in the Regulation of Education." New York, NY: Routledge]. New forms of datafication and automation enable governments and other powerful stakeholders to draw from the past to construct images of educational futures in order to steer the present. This paper examines the competing conceptions of time and temporality that AI posits for policy and practice when used to anticipate educational futures. We argue that most educational futures are already delineated, and machinic expressions of time are the chronologies, habits, and memories that the educated subject inhabits rather than produces. If resetting educational habits and memories can be an alternative to algorithmic anticipations of education then we believe, paradoxically, that machines may help to reset them by accelerating them.
Abstractor: As Provided
Entry Date: 2020
Accession Number: EJ1265602
Database: ERIC
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: The use of data to govern education is increasingly supported by the use of knowledge-based technologies, including algorithms, artificial intelligence (AI), and tracking technologies [Fenwick, T., E. Mangez, and J. Ozga. 2014. "Governing Knowledge: Comparison, Knowledge-Based Technologies and Expertise in the Regulation of Education." New York, NY: Routledge]. New forms of datafication and automation enable governments and other powerful stakeholders to draw from the past to construct images of educational futures in order to steer the present. This paper examines the competing conceptions of time and temporality that AI posits for policy and practice when used to anticipate educational futures. We argue that most educational futures are already delineated, and machinic expressions of time are the chronologies, habits, and memories that the educated subject inhabits rather than produces. If resetting educational habits and memories can be an alternative to algorithmic anticipations of education then we believe, paradoxically, that machines may help to reset them by accelerating them.
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