Students as data subjects (part I)

Outside of the home, schools are one of the most significant domains for young people when it comes to personal data. Schools have always generated, collected and used large amounts of student data, but with the increasing digitization and automation of many daily processes (i.e. attendance, medical records, consent forms etc.) they are now responsible for storing and securing huge amounts of sensitive personal data. Schools are understandably wary of the risk of data breaches with potentially serious consequences. This was brought home by a recent Australian case in which private medical information of students (including mental health issue and risk of suicide) was inadvertently shared with a student via her Google drive.

But there are other less obvious consequences for young people with regard to their personal data at school. As this post will go onto explain, personal data create data subjects. And as data subjects, young people potentially become ‘subject to’ a range of institutional and commercial processes that they may well be unaware of. In the context of schools this raises important questions, such as: What kind of ‘student’ is generated through data? How are these subjectivities created and who or what is creating them? And finally, what bearing will these subjectivities have on young people’s immediate and longer-term futures?

One focus of critical data studies is to examine subject formation within datafied systems and to understand how individuals (in this case young people) might be positioned and influenced. A subsequent focus is then identifying where spaces and opportunities for resistance might be found. These goals are central to our project as we seek to understand the impact of data on students, as well as the ways in which it might ‘done’ differently and more ethically.

 What are data subjects?

According to the European Union’s GDPR,a ‘data subject’is ‘any person whose personal data is being collected, held or processed’ [1]. Personal data can refer to anything from a person’s name and date of birth to their digital interactions and GPS co-ordinates. Subjectivationis the process of becoming a subject and obviously pre-dates the digital era.  For example, a child becomes a ‘student’ through the institutions of schooling, while a person becomes a ‘patient’ when admitted to hospital.

While it is fair to say that individuals had multiple subjectivities in the pre-digital era, in the digital age these have increased exponentially. One obvious example is the ways in which digital platforms generate subjects – e.g. Twitter generates Tweeters, YouTube generates YouTubers. Creating a profile on a platform is therefore a process of subjectivation. Whether it be creating a social media profile or being admitted to hospital, the specific technological and institutional arrangements mediate interactions and enact populations to create subjects (Ruppert, 2011).

Yet, all the while personal data are collected through our digitally-mediated actions and used to place us into categories that ‘code’ different aspects of our culture. Crucially, this ongoing process of categorisation shapes how an individual encounters the world. Cheney-Lippold (2011) describes this as a ‘soft biopower’ carried out through a series of dividing practices based on objectification and categorisation, which defines what ‘a population is and how it is discursively situated and developed’ (p.175). To complicate matters even further, it is important to remember that subjects are never fixed. Indeed, as any subject generates data they are continually (re)formed (Ruppert et al., 2017).

A key part of our project then is considering the array of subjectivities that are created for students through the data that is generated at school. A great deal of this data will be tied to the school’s learning management system (i.e. Compass) for attendance, assessment, reporting and so on. Yet if a teacher happens to be using commercial apps, like Class Dojo or Care Monkey, then their students are subject tothe protocols and processes adopted by these platforms. In this way, if the personal data generated through that platform is traded via data brokers then students unknowingly become subjects to new forms of power and control.

This direct connection between what goes on in a classroom and the global data economy is an important line of inquiry. Traditionally, one might have expected that school offers students a modicum of privacy to the commercial world. Yet once a user profile on a commercial app is created, students inevitably become subjects of economics and politics. Wendy Chun (2016) explains this neatly: ‘Internet users are curiously inside out – they are framed as private subjects exposed in public’ (p.12).

While it might be impossible to identify the array of subjectivities a student is tied to, it is important that we try. For example, auditing the software used in our case schools and analysing the End-User Licensing Agreements (EULA) + Terms of Service (ToS) might give us some indication of what becomes of the data generated. It is also important that we explore students’ awareness of their profiles and ‘data doubles’, as well as their capacity to influence and resist these processes of subjectification. While this is currently rationalised as an inevitable aspect of the datafied school, we have a responsibility to test and challenge this assumption.


Cheney-Lippold, J. (2011). A new algorithmic identity: Soft biopolitics and the modulation of control. Theory, Culture & Society, 28(6), 164-181.

Chun, W. (2016). Updating to Remain the Same: Habitual New Media. Cambridge, MA: The MIT Press.

Ruppert, E. (2011). Population objects: Interpassive subjects. Sociology, 45(218-233).

Ruppert, E., Isin, E., & Bigo, D. (2017). Data politics. Big Data & Society, 1-7.




[1]EU GDPR Compliant: