Over the past years, the global rise of increasingly automated digital technologies has signi­ficantly impacted the educational sector. This particularly includes the growing proliferation of data-intensive ‘personalized’ learning and monitoring techniques (learning analytics, early warning or drop-out prevention systems, etc., see e.g. Pinkus 2008), which build on prediction-making and intervention (e.g. automated feedback, data dashboards etc.). Automated techno­logies use a combination of data mining/analysis and designing/modelling (using data from the past), to create and govern both present learning settings (measured and captured live) and, on that basis, to anticipate upon desired futures (e.g. through algorithmically generated output). It is in that respect increasingly argued that algorithmically driven, automated technologies install new forms of anticipatory and predictive governance (Amoore, 2020; Hansen, 2015; Macken­zie, 2017). Common to all these types of governance, is their dataist gaze; that is, a gaze that (only) sees value in phenomena and entities when they contribute to data processing (Harari, 2017).

Even though educational research on these phenomena is slowly emerging, there still remains a huge lacuna in research that critically disentangles the design and working operations of automated technologies in their transformation of learning (Knox, Williamson & Bayne, 2020). Taking stock of the sparse available body of educational research (e.g. ibid.; Perrotta 2020, Gulson/Webb 2017; Gulson/Winzenberger 2020) we can identify three determining charac­teristics of automated learning designs: educational designs are programmed to (1) automati­cally predict the future based on present data practices (e.g. a student engaging on an assessment platform), (2)compare this prediction with a desired future (e.g. a student finishing the assessment with more than 50% correct answers), and (3)intervene in the sense of keeping or bringing stu­dents back „on track“, which means back onto the designed pathway of learning (e.g. though the application of nudging technologies). It is the combination of these three processes, and the concomitant process of looking for correlations in the data generated like­wise, that is commonly designated as machine learning. Machine learning technologies hereby continuously accelerate and refine the circle of data analysis, modelling, prediction and inter­vention, seeking to optimize the probability to achieve a desired and foreclosed future (Amoore, 2020).

However, despite these first emerging understandings and characteristics, there remains a crucial lack in our understanding of what such automatic, predictive and learning technologies are, as well as of what they do with educational actors (e.g., students, teachers), educational activities (e.g., learning, teaching), and education policy more broadly (cf. Fourcade & Gordon, 2020). To that effect, this symposium investigates how present-day digital learning-environ­ments are designed in such a way that they install specific forms of anticipatory and predictive governance, and what the distinctive educational qualities of such designed forms of gover­nance are then, precisely. The contributions in this symposium thus have a double finality. First, they allow us to better understand how what it is to learn in an automated environment takes different shape for students and pupils, whilst at the same time accounting for how auto­mated environments themselves learn in highly specific and contingent ways. Second, through of­fering fine-grained analyses of automated technologies, the contributions to this symposium propel our understanding of the educational specificity of newly emerging forms of governance through deploying automated technologies.

Amoore, L. (2020). Cloud Ethics: Algorithms and the Attributes of Ourselves and Others. Duke University Press. Fourcade, M., & Gordon, J. (2020). Learning Like a State: Statecraft in the Digital Age. Journal of Law and Political Economy, 1(1), 78-108. Gulson, K. N., & Webb, P. T. (2017). Mapping an emergent field of ‘computational education policy’: Policy rationalities, prediction and data in the age of Artificial Intelligence. Research in Education, 98(1), 14-26. Gulson, K. N., & Witzenberger, K. (2020). Repackaging authority: artificial intelligence, automated governance and education trade shows. Journal of Education Policy, 1-16. Hansen, H. K. (2015). Numerical operations, transparency illusions and the datafication of governance. European Journal of Social Theory, 18(2), 203-220. Harari, Y. N. (2016). Homo Deus: A brief history of tomorrow. Random House. Knox, J., Williamson, B., & Bayne, S. (2020). Machine behaviourism: future visions of ‘learnification’and ‘datafication’across humans and digital technologies. Learning, Media and Technology, 45(1), 31-45. Mackenzie, A. 2017. Machine Learners: Archaeology of a Data Practice. London: MIT Press. Perrotta, C. (2020). Programming the platform university: Learning analytics and predictive infrastructures in higher education. Research in Education, 0034523720965623. Pinkus, L. (2008). Using early-warning data to improve graduation rates: Closing cracks in the education system. Washington, DC: Alliance for Excellent Education.