Jarke & Breiter (2019) on the datafication of education

Juliane Jarke and Andreas Breiter start with a neat definition of ‘datafication’ – the ways in which new technological and computational techniques “introduces new means to measure, capture, describe and represent social life in numbers” (p.1)

They also remind us that the rise of digital data in education in entwined with the rise over the past twenty years or so of ‘governing by numbers’ in education policy and, as such, national variations in how data is used to “measure, monitor and control” school systems.

However, they also take care to remind us of the novelties and new promises of digital data that is generated at scale, circulated at speed and processed on a continuous ‘real-time’ basis. In this sense:

“digital data allow for the analysis of different educational practices to a degree of complexity not previously possible and to a much greater extent, as they can be very detailed, cover a more complete scope and can be flexibly combined … digital data not only serve to support decisions, but also fundamentally change the organisation of learning and teaching” (p.3)


The bulk of the short paper is then taken up us a rapid-fire run through what the authors frame as the “ambivalent and at times contradictory consequences” (p.5) of digital data in education. These include the following points of contention:

  • Datafication leads to new spatio-temporal entanglements. As the authors contend: “The boundaries of educational institutions as learning places change when activities within the learning environment come to be represented on digital platforms and measurable learning outcomes are translated into assessment data based on standardised tests of students, teachers, professors or an entire institution” (p.3).  This is especially noticeable in terms of shifting temporality of school – for example, the ways in which the traditional rhythm of the academic year and the school term is warped by the accelerated data-driven cycle of immediate feedback, real-time reporting and so on.
  • Datafication transforms translocal relationships. For example, altered relationships between teachers and students, or between parents and school authorities. At the same time, digital data hastens the process of schools ‘bringing in’ of “different, previously distant actors” (p.3) – ranging from commercial platform providers to local data mediators.
  • The datafication of education allows for and requires new forms of participation. Everyone in a school community is now involved in a range of new forms ‘data work’. For some, this can be a welcome and beneficial deeper engagement with school processes (for example, the increased parental oversight of classroom activities through real-time reporting apps). In other instances, this might be experienced in more onerous ways – for example, teachers facing the bureaucratic routine of report/receiving/responding to data at the end of term regardless of other reflection, feedback and analysis work that might have taken place already.
  • The benefits of digital data require competences to interpret data (critically) – this promotes a new digital divide and increases inequalities. The availability of data within an school inevitably leads to an individualisation of responsibility to ‘become informed’. However, this requires considerable levels of data competency and what we refer to elsewhere as ‘data imagination’. A lot of the tools and techniques that are deployed in schools to ‘do data’ are not configured for educational users. This sees teachers, parents and students having to interpret and make sense of data outputs designed to be engaged with by data professionals. Some people are going to be better positioned to do this than others.
  • The datafication of education leads to a redistribution of agency across socio-technical networks. Within schools, a cadre of individuals who are positioned as ‘data experts’ (or simply data gatekeepers) can quickly assume disproportionate levels of organisational power. At the same time, agency is also often handed over to the data itself – for example, as algorithms are used to make decisions on behalf of the teacher, and so on.
  • Digital data practices may allow for new forms and possibilities of monitoring and surveillance, while at the same time promoting transparency. Here, for example, Janke and Breiter point to the juxtaposition of school authorities having a genuine interest in supporting school development while also unable to resist the realisation that data-tools and techniques can also be used as effective control instruments.


One of the most interesting elements of the paper was expanding the scope of data in education beyond talk of impacting on ‘decision-making’ to also include the notion of data impacting on ‘opinion-forming’. This idea of data now being a primary driver of what people think about education is worth expanding. The most interesting point here is that data can set the affective conditions through which we approach schooling. Data therefore informs the opinions of a whole range of actors – clouding their perceptions and expectations about education. For example, data can cause a politician feel that her country is number one in the world, but can also tip an already marginalised student into feeling that school really isn’t for them … despite their best efforts.

These latter thoughts are not reflected on at length, but certainly underpin the concluding point of the paper. In short, it is reasonable to argue that digital data does not work to simply describe education. Instead, digital data works to (re)define education in its own image. As the authors conclude:

The relationship between data and what they are meant to represent is recursive: Data are not ‘natural’ by-products of social actions, but must always be understood in the context of their origin and the affordances of the respective digital infrastructure. The datafication of education does not only transform education but also our understanding of education, of what is understood as ‘good education’, associated objectives and good practices” (p.5)


[Juliane Jarke & Andreas Breiter (2019) Editorial: the datafication of education, Learning, Media and Technology, 44(1):1-6]