Lyndsay Grant, Alison Oldfield & Ben Williamson
Data is becoming monstrous. The second Code Acts in Education seminar, in Edinburgh on 9 May 2014, focused on the ways in which different forms of data, and especially new forms of digital data, have been normalized in the organization of schools and in educational governance and policy work. In two of the presentations, by Jenny Ozga (University of Oxford) and Matt Finn (University of Durham), educational data was presented in terms of its monstrosity.
Jenny Ozga’s presentation, based on the ESRC project Governing by Inspection, explored how data systems frame the production and use of knowledge in governing education. In her analysis of the practices and uses of school inspection data, Jenny showed how knowledge and governing can be seen as mutually constitutive. The use of knowledge to govern education results in an emphasis on practical knowledge – knowledge that can be used to bring about change by identifying the levers for action. Knowledge, when used as a governing device, works to frame educational problems and solutions in particular politicized ways. A neoliberal system that emphasizes the role of competition in driving system efficiencies creates a demand for ‘objective’ information to inform ‘consumer’ choices necessary for the smooth operation of an educational quasi-market. ‘Big data’ enters into this political frame, seeking to persuade that it can provide solutions to this demand for information and knowledge. Thus we have, Ozga claimed, monstrous educational data systems that ‘require constant feeding.’ Moreover, such systems are enabled by data software products that have been programmed with increasingly autonomous powers—what Ozga termed ‘zombie systems somewhere between the dead and the living.’
Rendering the increasing volume of complex data intelligible and actionable has become a policy problem in educational governance, a problem that has given rise to the development of new centres and forms of expertise. Comparison has become a key logic for drawing out the lessons of complex data, and Ozga suggested that we now only really understand where we ‘are’ by comparison with others. A telling example, quoted from research interviews, showed how an EU official claimed to be able to better understand what made Scottish education uniquely Scottish, from a distance and in comparison to other nations, than would be possible from a position embedded within the Scottish system. This relational view of education calls into being something national by comparing and selecting out in an international context – a process that demands comparable data across national boundaries.
These processes of simplification and comparison could also be observed at the Department for Education in England. Here, a senior DfE official described:
a concept here in the Department which we’ve called ‘The Bridge’ where we corral all of this data and information and at a glance now across all local authorities in England you can go downstairs and look at a big screen and you can look across all the key performance areas and that’s actually across all the social care areas as well as education … So at that level we’re doing quite active performance management of the system and that’s quite a powerful tool.
The simplification, comparison, and decontextualization of data was here seen as a significant step towards performance management – overriding the need to understand local context or inevitable data errors that creep into such large data sets. It is only by simplifying and decontextualizing data that such knowledge about the overall system could be visualized without getting dragged into the minutiae of individual school circumstances. This data dashboard is then presumably informing the decisions that are made by policy workers in the DfE and the kind of data that they require schools to produce. In this way, Ozga argued, data here is not just representing, but actually coming to constitute education.
The work of data systems in governing education can be seen ‘up close and personal’ in the way they are mobilized in the work of Ofsted inspectors in England. Through her research, Ozga has identified a tension between the embodied judgments of inspectors through processes of observing and drawing on professional expertise, and the drive to use data to make more disembodied and ‘objective’ judgments. Inspectors are now increasingly required to play their part in a highly regulated and codified process, in which they must follow strict ‘algorithmic’ rules, with the scope for professional judgements based on experience sharply reduced. With RAISE Online (Reporting & Analysis for Improvement through school Self-Evaluation) data packs as the first port of call for any inspection process, and the basis for the first conversation with the head teacher, the data displaces individual professional judgement and dialogue with the school. School data, Ozga argued, is organized and coded before the inspector even arrives at the gates.
The use of data in governing, improving and regulating education has become taken for granted. Even embodied judgment-making, through school inspections and EU comparisons, is increasing coded and comes to resemble an algorithmic process. Big data and digital data appear to be accelerating the extensive use of data-based knowledge as a tool to govern education, with the rise of always-on ‘zombie’ data systems that are continually collecting and communicating data in the background activity of the school. Ozga’s key point here is that all this coded activity adds up to very political work. In the choice of what to measure, how to measure it, how often to measure it and how to present and interpret the results, we can trace political judgments about what education is, what it is for, and how national and international organizations seek to govern it.
Matt Finn shared findings from his doctoral research, which examines ‘the life of data’ and ‘data-based living’ in a secondary school. He has carried out an ethnographic study of how data flows through the management systems, classrooms and relationships in one particular secondary school in England. His research has identified that the current use of data in the school meant that a new data-based sense of ‘progress’ is fundamentally changing the relationships between students and teachers. For example, the way data was used in this school meant that students and teachers became increasingly responsible for each others’ futures because the achievement of both were dependent upon ‘progress’ as judged by data. Teachers must focus on demonstrating evidence of learning through the data produced through various performances.
In Finn’s ethnographic study of the life of data in school, he found that data formed part of students’ and teachers’ identities, regardless of how well they knew or understood where it came from or how reliable it was. Data in the school was often made public on display boards, and shared with pupils, where, Finn claimed, it appeared to have become the source for relationships and friendships. Indeed, Finn claimed that the idea of teachers ‘caring’ about pupils is increasingly being framed in schools as an issue with ‘getting them the data.’ Data, he argued, acts to engage pupils, and even to engender positive feelings and ‘affective atmospheres’ in schools. It is thoroughly threaded through the relations between pupils and teachers and is tied to the things that they do. Citing Louise Amoore, he claimed that ‘data live on to reverberate in the world.’ The influential, authoritative and public role that data plays in this relationship leads to the interesting question of ‘What does it mean to care in the context of data?’
A notable point in Finn’s presentation was the idea of the ‘pupil multiple.’ Drawing on Annemarie Mol’s notion of the ‘body multiple’ (which reveals how the human body is constantly reframed through different practices, forms of measurement, observation or treatment), Finn pointed out how measurement and observation data is constantly collected on every child. Thus a school can produce a ‘data double’ of each individual pupil—a digital rematerialization from a composite of their data traces. But since this data is always changing, proliferating and being amplified, the data-pupil can always be recomposed as new units of data are combined, amended, updated, and so on. This amounts to what Finn termed a ‘Frankensteining’ of pupil data, as the data are combined into multiple ‘monstrous’ creations. Teachers are likewise recomposed in systems of classroom evaluation and inspection, as both Matt Finn and Jenny Ozga demonstrated. Through the collection, analysis and visualization of data, schools are becoming coded spaces inhabited by shape-shifting versions of both pupils and teachers as data objects themselves.
Hi and thanks for the summary! I really enjoyed the day.
In making the comments I did following Jenny Ozga’s presentation I’d just like to clarify that I’m not arguing that data is in fact monstrous or even Frankensteinian but that I was reporting how others (in fact Lawn and Ozga, 2010) have talked about data as monstrous with a ’life of its own’. I realise this might not have been so clear on the day! For me this metaphor is instructive though because I understand the language of the monstrous to highlight concerns about the agency of data. This concern is that rather than acting as an impersonal and inert servant to a master’s desire that there is a risk both of ‘unnatural’ liveliness and concomitant dehumanisation but also again of something going out its ‘proper limits’, of being out of control. On the basis of the work I’ve done people do have these concerns but I think the agency of data is more ambiguous than this in practice. If that’s the case then the ‘pupil multiple’ need not be understood as a sinister creation. That said free to take the ideas as you think useful!
Thanks again for the interaction and for hosting the conversation.
Your talk was certainly nuanced around the data Matt, and your forthcoming articles reflect that really well. The metaphors around data, as monstrous or zombie-like or vampiric, are themselves interesting objects of study–a kind of data horror! Interestingly, of course, Frankenstein’s monster is itself a rather sympathetic figure that lives in search of companionship, friendship and care. Monsters, like data, can be endowed with feeling too.
Yes, that’s very true. I wonder coming back to the idea of care what it means to care for the pupil-multiple as we encounter them as people-in-front-of-us and as quantified selves which are more overtly distributed through the school in the form of data.