Datafying education: How digital assessment practices reconfigure the organisation of learning

In the past decade, an ever increasing trend to capture (social) life in numbers became a prominent instantiation of the so-called ‘audit society’ (Power 1999). With this turn, almost all aspects of social life have become measured and quantified. This datafication of social life raises expectations concerning increased transparency, accountability and civic participation but also associated fears with respect to surveillance, privacy issues, a data literacy divide and control (Kitchin 2014; Borgman 2015; Gitelman 2013). Datafication as a trend of a changing media environment affects many social domains significantly, and one of the most noticeable of these are organisations of education (Piety 2013). It relates for example to schools’ performances and student achievements which are compared on a national and international scale; it may affect salaries of teachers and school managers which are adjusted according to test scores as well as decision-making of parents for school choice or communication and control of teachers.

Assessments have always been decisive features of learning and are pervasive within education: School exams evaluate pupils; achievement tests measure and select students for higher education, school performance studies such as PISA measure and compare whole educational systems. With the ever growing use of information and communication technologies to support the organisation of learning and teaching new devices for monitoring, evaluating, and ranking the performance of individual learners and of educational institutions/systems have become available. They range from computer-based tests to learning analytics on large-scale data in complex information systems. They allow the ‘recording, storage, manipulation and distribution of data in digital form’ (Selwyn 2015: 64). Digital data are distinct from pre-digital forms as they may be exhaustive in scope, highly detailed and can be combined in a flexible manner and at different aggregation levels, bringing together ‘datasets of different times, from different places or gathered at different times’ (Parks 2014: 356). Such possibilities have always existed on a small scale, but new data infrastructures for accountability (Anagnostopoulos et al. 2013) and algorithmic capabilities allow for analytics on an ‘unprecedented complexity and scope’ (Parks 2014: 356). Within the educational context, more data and more heterogeneous data are being
generated—deliberately—for monitoring, surveillance or evaluation purposes but also—
automatically—through routine operations of digital devices and systems (Selwyn 2015:
65).