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|Appears in Collections:||Faculty of Social Sciences Journal Articles|
|Peer Review Status: ||Refereed|
|Title: ||Who owns educational theory? Big data, algorithms and the expert power of education data science|
|Author(s): ||Williamson, Ben|
|Issue Date: ||1-May-2017|
|Citation: ||Williamson B (2017) Who owns educational theory? Big data, algorithms and the expert power of education data science. E-Learning, 14 (3), pp. 105-122. https://doi.org/10.1177/2042753017731238|
|Abstract: ||‘Education data science’ is an emerging methodological field which possesses the algorithm-driven technologies required to generate insights and knowledge from educational big data. This article consists of an analysis of the Lytics Lab, Stanford University’s laboratory for research and development in learning analytics, and the Center for Digital Data, Analytics and Adaptive Learning, a big data research centre of the commercial education company Pearson. These institutions are becoming methodological gatekeepers with the capacity to conduct new forms of educational research using big data and algorithmic data science methods. The central argument is that as educational data science has migrated from the academic lab to the commercial sector, ownership of the means to produce educational data analyses has become concentrated in the activities of for-profit companies. As a consequence, new theories of learning are being built-in to the tools they provide, in the shape of algorithm-driven technologies of personalization, which can be sold to schools and universities. The paper addresses two themes of this special issue: (1) how education is to be theorized in relation to algorithmic methods and data scientific epistemologies and (2) how the political economy of education is shifting as knowledge production becomes concentrated in data-driven commercial organizations.|
|DOI Link: ||10.1177/2042753017731238|
|Rights: ||Publisher policy allows this work to be made available in this repository. Published in E-Learning and Digital Media by SAGE. The original publication is available at: https://doi.org/10.1177/2042753017731238|
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