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Appears in Collections:Faculty of Social Sciences Journal Articles
Peer Review Status: Refereed
Title: Digital education governance: data visualization, predictive analytics, and 'real-time' policy instruments
Author(s): Williamson, Ben
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Keywords: big data
policy instruments
predictive analytics
Issue Date: 2016
Date Deposited: 7-May-2015
Citation: Williamson B (2016) Digital education governance: data visualization, predictive analytics, and 'real-time' policy instruments. Journal of Education Policy, 31 (2), pp. 123-141.
Abstract: Educational institutions and governing practices are increasingly augmented with digital database technologies that function as new kinds of policy instruments. This article surveys and maps the landscape of digital policy instrumentation in education and provides two detailed case studies of new digital data systems. The Learning Curve is a massive online data bank, produced by Pearson Education, which deploys highly sophisticated digital interactive data visualizations to construct knowledge about education systems. The second case considers ‘learning analytics’ platforms that enable the tracking and predicting of students’ performances through their digital data traces. These digital policy instruments are evidence of how digital database instruments and infrastructures are now at the centre of efforts to know, govern and manage education both nationally and globally. The governing of education, augmented by techniques of digital education governance, is being distributed and displaced to new digitized ‘centres of calculation’, such as Pearson and Knewton, with the technical expertise to calculate and visualize the data, plus the predictive analytics capacities to anticipate and pre-empt educational futures. As part of a data-driven style of governing, these emerging digital policy instruments prefigure the emergence of ‘real-time’ and ‘future-tense’ techniques of digital education governance.
DOI Link: 10.1080/02680939.2015.1035758
Rights: © 2015 The Author(s). Published by Taylor & Francis This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Permission is granted subject to the terms of the License under which the work was published. Please check the License conditions for the work which you wish to reuse. Full and appropriate attribution must be given. This permission does not cover any third party copyrighted material which may appear in the work requested.
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