This paper draws on Zuboff’s idea of the ‘division of learning’ (2019, p175) to examine the ways ‘learning’ is being (re)configured in educational practice through the increasing pre-valence of data-driven education technologies. Further work is needed to situate this idea in education, where data-driven technologies are usually developed with an explicit claim of ‘enhancing’ student learning (see Bayne 2015), while at the same time undertaking ‘machine learning’ as a form of technical self-enhancement. Data-driven education therefore appears to be a particularly salient example of the need to account for ‘both humans and machines or human-machine relations’ (Mackenzie 2017, p6) in the study of ‘learning’ in contemporary times. This paper will discuss two ways in which the idea of the ‘division of learning’ might be productive in the critique of education. Firstly, in highlighting public/private tensions in which ‘authority over what constitutes valuable and worthwhile knowledge’ (Williamson 2019) appears to be increasingly allocated to ‘personalising’ machines. Secondly, through exposing underlying conflicts between, on the one hand, notions of purpose as foundational to education (Biesta 2012), and on the other, the ‘formal indifference’ (Zuboff 2015, p79) of data processing companies, and their general interest in ‘everydayness qua data’ (Constantiou and Kallinikos 2014).

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