The importance of data for educational processes in schools such as teaching and learning as well as for educational administration has increased throughout the last decade. This has an impact on how education is measured, managed and controlled. ‘Governing by numbers’ (Grek 2009) has become a new paradigm in educational policy. National and international student assessments, standardised achievement tests, school inspections and rankings are part of new forms of educational governance on all levels (Altrichter 2010). On the macro level, public pressure on changing education policy are often enforced by international non-governmental organizations like the OECD and are based on data. This can be observed since the first publication of PISA results in the 1990s. Martens and others explained different reactions of nation states to these pressures (Martens/Jacobi 2010) – from adoption of achievement tests in national education policies, to ignoring it. This is part of a larger movement of standardization in education, output measurement and accountability. While the political perspective has been studied intensively, the underlying data practices of key stakeholders (students, teachers, parents, administrators) are under-researched (Breiter 2016). This relates to the meso level of school administrations by introducing methods of new public management for budget control, benchmarks and goals to measure effectiveness. The role of districts and educational authorities in handling data and using data for accountability varies significantly between European countries. This makes an international perspective necessary. On the micro level of the school, different forms of data use have been identified on both the managerial level of principals to teachers on the classroom level (Schildkamp/Poortman 2015). Learning analytics (Papamitsiou/Economides 2014) are promoted as a powerful tool for better (individualised) learning and student support. The underlying algorithms and the ways in which data are produced by data providers, statisticians and software developers are hardly understood (Eynon 2013, Williamson 2014). The symposium will address different perspectives and empirical evidence across different EU countries: from data use of teachers (Schildkamp) and parents for school choice (Jarke/Breiter) to social implications of big data (Eynon) and software-based measurement of emotional learning (Williamson).
Altrichter, H. (2010): Theory and Evidence on Governance: conceptual and empirical strategies of research on governance in education. European Educational Research Journal 9 (2), 147-158.
Breiter, A. (2016): Datafication in education: a multi-level challenge for IT in educational management. in: T. Brinda, D. Passey, (eds.), Stakeholders and Information Technology in Education. Berlin: Springer
Eynon, R. (2013). The rise of Big Data: what does it mean for education, technology, and media research? Learning, Media & Technology, 38(3), 237-240.
Grek, S. (2009): Governing by Numbers: The PISA ‘Effect’ in Europe. Journal of Education Policy 24 (1), 23-37.
Martens, K.; Jakobi, A. P. (2010): Mechanisms of OECD Governance - International Incentives for National Policy Making. Oxford: Oxford University Press.
Papamitsiou, Z.; Economides, A. A. (2014): Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence. Educational Technology & Society 17 (4), 49-64.
Schildkamp, K., & Poortman, C. L. (2015). Factors influencing the functioning of data teams. Teachers College Record, 117(5).
Williamson, B. (2014): Governing software: networks, databases and algorithmic power in the digital governance of public education. Learning, Media and Technology, 1-23.