A site for Critical Data and Education (an MSCDE course)

Tag: Week 11 (page 1 of 1)

On ‘Governing’

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, http://blog.ukdataservice.ac.uk/will-more-data-change-the-lives-of-lgbtq-people-in-the-uk/ 

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.


Block: ‘Governing’ with data / Week 11

This week, I counted the number of times the word ‘data’ appears in my blog posts. Excluding references, I used this word 96 times in the posts prior to this one. In the video below, 96 drops of water are dropped into a glass to represent this word count (admittedly a very simple piece of data to work with this week). 

Prinsloo (2020) describes a ‘data imaginary’ in higher education that provides and legitimises a particular vision of what we can do with data. This imaginary also serves to expand and intensify the datafication of education, by projecting promises and fears through which neoliberal data practices can reach further into our lives (Beer 2019). How might we disentangle ourselves from this imaginary in order to imagine alternative visions of data?

Beer posits six features of a specific ‘data imaginary’: one that is speedy, accessible, revealing, panoramic, prophetic and smart (2019). By identifying features of current data imaginaries, we might clarify features of alternative imaginaries – that are un-speedy, or un-smart, for example – and the socio-political positions at play in these. I am thinking of Zeffiro’s identification of a present ‘reproductive data futurism’ in order to imagine a ‘queer futurity’ of data (2019), or how Data for Black Lives identifies methods of data weaponisation against Black people to then imagine what actions are required to create a world with #NoMoreDataWeapons (Watson-Daniels, 2021).

Throughout these weekly exercises, I’ve grown tired of the act of measuring (I don’t know how Lupi and Posavec kept this up for a year). I identified three data features I had grown tired of recording: 

  • Time (plotting data across linear time)
  • Activity (rendering actions, thoughts and the body as data points, situated in linear time)
  • Usefulness (What other time or activity can I compare this data to? Or, what can I do with this data?)

The water performance is an attempt to imagine deliberately un-useful ways of recording time and activity. The 96 data points – water droplets – exist only for the amount of time they take to fall from the dropper to the glass. The time in which the data droplets exist doesn’t relate to the time in which the original actions took place (the typing of the word ‘data’). The resulting water remains measurable but the only material relationship between this water-as-data and the act of writing is my hand, operating the unseen keyboard and then the dropper.

(After recording this, I noticed the camera couldn’t see the droplets, only the effect when they land. I also didn’t notice, while I was focused on counting, that the performance has been augmented by the sounds of a family member calling out to a cat and packing away dishes. I am left considering the unwitting effects and affects of the ways we represent data, and of the staging of data imaginaries.)


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.

Beer, D. 2019 The data gaze, Sage.

Zeffiro, A., 2019. ‘Towards a queer futurity of data’, Journal of Cultural Analytics, 1(1), DOI: 10.22148/16.038

Watson-Daniels, J., 2021, Introducing #NoMoreDataWeapons, 26 February, https://blog.d4bl.org/introducing-nomoredataweapons/