Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/22657
Appears in Collections:Faculty of Social Sciences Journal Articles
Peer Review Status: Refereed
Title: Digital methodologies of education governance: Pearson plc and the remediation of methods
Authors: Williamson, Ben
Contact Email: ben.williamson@stir.ac.uk
Keywords: Data
data science
digital methods
methodology
Pearson
soft governance
software
Issue Date: Jan-2016
Publisher: SAGE
Citation: Williamson B (2016) Digital methodologies of education governance: Pearson plc and the remediation of methods, European Educational Research Journal, 15 (1), pp. 35-53.
Abstract: This article analyses the rise of software systems in education governance, focusing on digital methods in the collection, calculation and circulation of educational data. It examines how software-mediated methods intervene in the ways educational institutions and actors are seen, known and acted upon through an analysis of the methodological complex of Pearson Education’s Learning Curve data-bank and its Center for Digital Data, Analytics and Adaptive Learning. This calls for critical attention to the ‘social life’ of its methods in terms of their historical, technical and methodological provenance; their affordances to generate data for circulation within the institutional circuitry of Pearson and to its wider social networks; their capacity to configure research users’ interpretations; and their generativity to produce the knowledge to influence education policy decisions and pedagogic practices. The purpose of the article is to critically survey the digital methods being mobilized by Pearson to generate educational data, and to examine how its methodological complex acts to produce a new data-based knowledge infrastructure for education. The consequence of this shift to data-based forms of digital education governance by Pearson is a challenge to the legitimacy of the social sciences in the theorization and understanding of learning, and its displacement to the authority of the data sciences.
Type: Journal Article
URI: http://hdl.handle.net/1893/22657
DOI Link: http://dx.doi.org/10.1177/1474904115612485
Rights: This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Affiliation: Education

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