Around the world, digital data have become increasingly present in education policy and governance, thus significantly contributing to and also changing how educational reality as well as professional (i.e. evidence-based) acting are (re)constructed (e.g. Bradbury 2019, Hartong 2016, Landri 2018, Lawn 2013, Williamson 2017). Hereby, data infra­structures, software and algorithms are increasingly affecting the organization, manage­ment and monitoring of edu­cation (e.g. Selwyn 2015), while a growing proliferation of ‘personalized’ digital learning technologies, which feed such monitoring tools, foster a direct link between teaching/learning activities and (at best: real-time and adaptive) governmental action (Williamson, 2017).

Globally, such technologies are often celebrated for enhancing learning motivation and fun in the classroom, releasing admi­nistrative burden, showing students´ needs at a glance etc. – thus seemingly helping teachers to concentrate on their “real” pedagogical work. Such an instru­mental perspective, however, tends to neglect what a growing body of studies has critically examined as the politics of data (Ruppert et al. 2017). This includes powerful, often invisibi­lized mechanisms of nor­malization and performativity, which deeply affect the (re)fabrication of educational know­ledge, teachers’/stu­dents´ subjectivities, consciousness and identity (Brad­bury/Roberts-Hol­mes 2016, Lewis/Hol­loway 2019, Manolev et al. 2018). However, so far there is still a very limited body of research, which actually focuses on how exactly in the field of education such normalization and performativity mechanisms (which indeed may come along with motivation or fun) are inscribed in (digital) data tools, gamified designs, surveillance or nudging techniques (Andrejevic/Sel­wyn 2019, Manolev et al. 2018), but also manifest in surrounding policies such as teacher training, low/high stakes accounta­bility practices (Brad­bury/Roberts-Hol­mes 2016, Lewis/ Holloway 2019), as well as a growing amount of the EdTech industry´s teacher recruitment activities. Additionally, while such developments can be found in very different educational sys­tems and cultural contexts (Hartong 2016), it still seems im­portant to equally acknowledge the question of how exactly they manifest across, but also within such contexts and why (e.g. when considering distinct historical statuses of the teaching profession).

Following this line of argumentation, the goal of this symposium is twofold: (1) to foster international exchange regarding studies on the datafication of education, including conceptual and methodological considerations, with a particular focus on normalization and perfor­mativity; (2) to think together about how to better connect such critical data politics research with educational communities regarding the question of how to act upon (or against) these politics, so how to consider this angle of data tools together with potential benefits such tools may have (what we describe as critical data literacy transfer).

To address these goals, the symposium brings together qualitative studies/presentations from three different country contexts (Germany, UK and Australia), including both the schooling and the early child­hood education sector, as well as different contexts of datafication (e.g. classroom management, curriculum development, monitoring or assessment). Building on the three pre­sentations, this symposium will be rounded out by a discussion input, which focuses on questions of critical data literacy transfer, and which will be followed by an extended time slot for Q&As as well as discussion with the audience.

Andrejevic, M., N. Selwyn (2019): Facial recognition technology in schools: critical questions and concerns, Learning, Media and Technology. Online First. Bradbury, A. (2019). Datafied at four: the role of data in the ‘schoolification’of early childhood education in England. Learning, Media and Technology, 44(1), 7-21. Bradbury, A., Roberts-Holmes, G. (2017). The datafication of primary and early years education: Playing with numbers. Routledge. Hartong, S. (2016) Between Assessments, Digital Technologies, and Big Data. European Educational Research Journal 15(5): 523–536. Landri, P. (2018): Digital Governance of Education: Technology, Standards and Europeanization of Education. Bloomsbury Publishing. Lawn, M. (2013) The Rise of Data in Education Systems. Oxford: Symposium Books. Lewis, S., J. Holloway. 2019. Datafying the teaching ‘profession’: Remaking the professional teacher in the image of data. Cambridge Journal of Education 49 (1):35-51 Manolev, J., A. Sullivan, R. Slee (2018): The datafication of discipline: ClassDojo, surveillance and a performative classroom culture, Learning, Media and Technology, DOI: 10.1080/17439884.2018.1558237. Ruppert, E., E. Isin, D. Bigo (2017): Data politics. Big Data & Society 4(2): 1–7. Selwyn, N. (2015): Data entry: Towards the critical study of digital data and education. Learning, Media and Technology 40 (1):64-82. Williamson, B. (2017): Big data in education: The digital future of learning, policy and practice. Los Angeles: Sage.