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The use of data to govern education is increasingly supported by the use of knowledge-based technologies, including algorithms, artificial intelligence (AI), and tracking technologies [Fenwick, T., E. Mangez, and J. Ozga. 2014. Governing Knowledge: Comparison, Knowledge-Based Technologies and Expertise in the Regulation of Education. New York, NY: Routledge]. New forms of datafication and automation enable governments and other powerful stakeholders to draw from the past to construct images of educational futures in order to steer the present. This paper examines the competing conceptions of time and temporality that AI posits for policy and practice when used to anticipate educational futures. We argue that most educational futures are already delineated, and machinic expressions of time are the chronologies, habits, and memories that the educated subject inhabits rather than produces. If resetting educational habits and memories can be an alternative to algorithmic anticipations of education then we believe, paradoxically, that machines may help to reset them by accelerating them. [ABSTRACT FROM AUTHOR]

Education is undergoing various transformations due to new data-driven educational technologies and the management of educational data through data infrastructures. These technologies are frequently promoted to parents and the profession as being ‘revolutionary’ because they represent a new generation of learning. While computer adaptive tests may arguably improve various efficiencies, the argument that they will revolutionise education requires evaluation. In this paper we draw on the philosophy of Gilles Deleuze and Felix Guattari to theorise (a) the desire for data amongst policymakers and (b) the effects of data infrastructures as systems that coordinate educational thought. We argue that, rather than revolutionising learning as promised, datafication in computer-based modes merely offers more intense expressions of longstanding possibilities for learning. We describe three types of events—breaks, cracks and ruptures—and argue that data-events translate cracks (imperceptible changes that constitute learning) into breaks (information), but either cannot generate rupture (difference) or represent rupture as error. However, the intensification of learning through datafication may, we suggest, rupture educational thought more broadly. [ABSTRACT FROM AUTHOR]