Block: ‘Governing’ with Data / Summary

Approaches to governing and policymaking in education are increasingly reliant on data-based processes, infrastructures and regimes (Williamson 2017). Data-driven “instruments of comparative analysis” claim to identify insights (Bates 2016, p.4), to increase transparency, and to help “in sorting things out” (Ozga 2016, p.78). These claims are targeted at and promoted by an increasingly diffused network of government, non-government and corporate “experts” involved in the making of educational policy. The concurrent turns towards data and “fast policy” (Williamson 2017) work in a symbiotic way in the education sector, each working to expand the other’s reach, fueled by a specifically colonial ‘data imaginary’ (Prinsloo 2020) and large-scale processes of standardisation, quantification and classification (Anagnostopoulos et al. 2013). 

Sometimes the relationship between policy and data has the ring of a salesman with his sales kit; showing up on your doorstep, identifying a problem you have and offering to sell you the solution. “All for the low low cost of…!” (or, talking about data, “for the very transparent and ethical collection of…!”)

This is not to say that data and policy can’t be used to solve problems or improve educational environments and outcomes. For example, clear school policies that protect against homophobia/transphobia contribute to a safer environment for LGBTQ+ students (Jones & Hillier 2012) and the collection of demographic data on sexual orientation and gender identity can assist in the development of such policies (Crowhurst & Emslie 2014). Yet the requirement to provide ‘evidence’ of our lives through data to receive recognition from a cis/heteronormative policy regime is problematic for queer and trans people (Guyan 2021). The benefits and harms of data use in governing education are not as clear cut as the sales pitch would make it seem.

My visualisations this block were initially interested in the problem-solving that data and policy promise. Could data help us calculate the distributed carbon costs of online education? Could data help us predict when a student requires mental health support? The solutions I considered require the collection of data from individuals to be used as evidence for governance. This reflects a shift in interest from the accounting of an institution’s datasets to an “intimate analytics” of the individual (Williamson 2017). Individual performance data is offered up for tracking and comparison against algorithmically determined norms, shifting power from local teachers and students to national and global networks of policymakers (Fontaine 2016). If what counts is what can be counted, then individual students and teachers have to offer themselves up to be counted in order to be ‘seen’ and have their needs addressed by governing bodies and infrastructures.

In my final visualisation, I tried to visualise something that didn’t claim to solve any problems. I turned to live performance rather than hand-drawing (breaking the rules of the blog!) to play with ways of ‘capturing’ data that eschew the processes of measurement that drive the shift of “informatic power” and agency from individuals to infrastructures (Anangopoulos et al. 2013). Still, in order to show you the performance existed, I had to record it. Thus I found myself in a familiar position – having to render experience visible through digital data in order for someone else to see it. 


Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. 2013. Introduction: Mapping the Information Infrastructure of Accountability. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds.) The Infrastructure of Accountability: Data use and the transformation of American education. Harvard Education Press.

Bates, A., 2016. Transforming Education: Meanings, myths and complexity. Routledge.

Crowhurst, M. and Emslie, M. 2014. Counting queers on campus: Collecting data on queerly identifying students. Journal of LGBT Youth, 11(3), pp.276-288.

Fontaine, C. 2016. The Myth of Accountability: How Data (Mis)Use is Reinforcing the Problems of Public Education, Data and Society Working Paper 08.08.2016.

Guyan, K. 2021. Will more data change the lives of LGBTQ people in the UK?, 11 February, 

Jones, T.M. and Hillier, L. 2012. Sexuality education school policy for Australian GLBTIQ students. Sex Education, 12(4), pp.437-454.

Ozga, J. 2016. Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15(1) pp.69-81.

Prinsloo, P. 2020. Data frontiers and frontiers of power in (higher) education: a view of/from the Global South. Teaching in Higher Education, 25(4) pp.366-383.

Williamson, B. 2017. Big Data in Education: The digital future of learning, policy and practice. Sage.