(2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology. Ahead of Print.

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.

(2020). The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education: Vol. 25, The datafication of teaching in Higher Education: critical issues and perspectives, pp. 351-365.

‘Platform capitalism’ and ‘surveillance capitalism’ describe the new business model and market form of the web. These forms of digital capitalism have begun to infuse the higher education landscape by merging with existing political demands for universities to become more data-driven, competitive, and market-focused. This article presents a case study of the education business Pearson and its expansion of the digital platform as a model for global higher education reform. A key ‘edu-business’ in the ‘global education industry’, Pearson has pivoted to online program management platforms, on-demand digital learning platforms, and intensive mobilization of data analytics to enact its strategic business priorities. These developments position Pearson as an authoritative source for both the evaluation and valuation of HE as a market. The analysis foregrounds the micro-processes involved as Pearson has sought to make, exploit, and maintain market opportunities in HE, demonstrating how the market form of digital capitalism is being reproduced by commercial power-players in the global higher education industry. As an actor in the sociotechnical arrangement of markets, the digital platform is a key participant in HE reforms that are intended to align the public mission of universities with the private interests of digital capitalism.

This paper examines visions of ‘learning’ across humans and machines in a near-future of intensive data analytics. Building upon the concept of ‘learnification’, practices of ‘learning’ in emerging big data-driven environments are discussed in two significant ways: the training of machines, and the nudging of human decisions through digital choice architectures. Firstly, ‘machine learning’ is discussed as an important example of how data-driven technologies are beginning to influence educational activity, both through sophisticated technical expertise and a grounding in behavioural psychology. Secondly, we explore how educational software design informed by behavioural economics is increasingly intended to frame learner choices to influence and ‘nudge’ decisions towards optimal outcomes. Through the growing influence of ‘data science’ on education, behaviourist psychology is increasingly and powerfully invested in future educational practices. Finally, it is argued that future education may tend toward very specific forms of behavioural governance – a ‘machine behaviourism’ – entailing combinations of radical behaviourist theories and machine learning systems, that appear to work against notions of student autonomy and participation, seeking to intervene in educational conduct and shaping learner behaviour towards predefined aims.

The purpose of this review was to assess the advancement of applications for physiologically based pharmacokinetic (PBPK) modeling in various therapeutic a