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In Applied AI, or 'machine learning', methods such as neural networks are used to train computers to perform tasks without human intervention. In this article, we question the applicability of these methods to education. In particular, we consider a case of recent attempts from data scientists to add AI elements to a handful of online learning environments, such as Khan Academy and the ASSISTments intelligent tutoring system. Drawing on Science and Technology Studies (STS), we provide a detailed examination of the scholarly work carried out by several data scientists around the use of 'deep learning' to predict aspects of educational performance. This approach draws attention to relations between various (problematic) units of analysis: flawed data, partially incomprehensible computational methods, narrow forms of 'educational' knowledge baked into the online environments, and a reductionist discourse of data science with evident economic ramifications. These relations can be framed ethnographically as a 'controversy' that casts doubts on AI as an objective scientific endeavour, whilst illuminating the confusions, the disagreements and the economic interests that surround its implementations.

This paper argues that methods used for the classification and measurement of online education are not neutral and objective, but involved in the creation of the educational realities they claim to measure. In particular, the paper draws on material semiotics to examine cluster analysis as a 'performative device' that, to a significant extent, creates the educational entities it claims to objectively represent through the emerging body of knowledge of Learning Analytics (LA). It also offers a more critical and political reading of the algorithmic assemblages of LA, of which cluster analysis is a part. Our argument is that if we want to understand how algorithmic processes and techniques like cluster analysis function as performative devices, then we need methodological sensibilities that consider critically both their political dimensions and their technical-mathematical mechanisms. The implications for critical research in educational technology are discussed.

This paper contends that powerful techniques to manipulate data, enabled by technological and economic developments, can be easily co-opted to serve the restrictive frameworks of hyper-controlling, managerial accountability that characterise current cultures of summative assessment in education. In response to these challenges, research is urgently needed to increase our understanding of the impact that assessments have on individuals and society. The paper concludes that social research ought to contribute to the identification of responses--educational, technological and political--that can minimise inequalities and potential abuses through the encouragement of data literacy across society. (Contains 2 notes.)

This paper argues that methods used for the classification and measurement of online education are not neutral and objective, but involved in the creation of the educational realities they claim to measure. In particular, the paper draws on material semiotics to examine cluster analysis as a 'performative device' that, to a significant extent, creates the educational entities it claims to objectively represent through the emerging body of knowledge of Learning Analytics (LA). It also offers a more critical and political reading of the algorithmic assemblages of LA, of which cluster analysis is a part. Our argument is that if we want to understand how algorithmic processes and techniques like cluster analysis function as performative devices, then we need methodological sensibilities that consider critically both their political dimensions and their technical-mathematical mechanisms. The implications for critical research in educational technology are discussed. [ABSTRACT FROM AUTHOR]

This paper contends that powerful techniques to manipulate data, enabled by technological and economic developments, can be easily co-opted to serve the restrictive frameworks of hyper-controlling, managerial accountability that characterise current cultures of summative assessment in education. In response to these challenges, research is urgently needed to increase our understanding of the impact that assessments have on individuals and society. The paper concludes that social research ought to contribute to the identification of responses – educational, technological and political – that can minimise inequalities and potential abuses through the encouragement of data literacy across society. [ABSTRACT FROM AUTHOR]