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

Category: Governing (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/


Block: ‘Governing’ with data / Week 10

This week, I recorded the times I got out of bed each morning and the times I started work each day. I wanted to visualise the relationship between sleeping in and starting work on time while I work from home (Figure 1).

It looks like I’m more likely to sleep in when working from home. For me, this has an affect on the temporal and spatial aspects of work. The time and space of work feels more bendy, stretchy and relative (“I’ll just push back my start time” or “I’ll just answer these emails from bed”). On Friday, when I went to campus, it was like time and space ‘snapped back’ to something more fixed or intentional.

Figure 1: Get out of bed!

Links between sleep patterns and mental health are well documented. Monitoring sleep patterns like this might be a useful indicator for when an intervention into one’s health or sleep hygiene is required.

Mental health interventions were on my mind this week as I read about the University of Edinburgh’s investigation into the support provided to Romily Ulvestad prior to her death in 2020. This led me to read about ‘early indicator’ systems universities use to indicate when a student is in need of academic or mental health support. It is no surprise, after reading about the turn towards ‘future-tense’ governance and ‘fast policy’ (Williamson 2017), that data-driven ‘early indicator’ systems are being tested to track individual students’ mental health using multiple sources of continuous data, including learning analytics (Foster 2019, Office for Students 2019). I wonder how useful learning analytics are for this – academic participation is not always correlated to signs of worsening mental health (although this appeared to be an aspect of Ulvestad’s case). I am also wary of universities accessing personal data to drive these predictive systems. This week’s data is not something I would ongoingly share as a worker or a student (although others might be comfortable doing this, if they thought the collecting body would use that data responsibly).

Overall, I think it’s a mistake to turn to predictive data-driven systems to indicate when a student is in need of mental health support. These are expensive distractions from the difficult policy and governance work that’s needed to address the issues students encounter when trying to get help from under-resourced and over-burdened student support systems. 


Foster, E 2019 ‘Can learning analytics warn us early on mental health?’, WonkHE, 18th July https://wonkhe.com/blogs/can-learning-analytics-warn-us-early-on-mental-health/

Office for Students 2019, Innovation, partnership and data can help improve student mental health in new £14m drive, viewed 20th March 2021, https://www.officeforstudents.org.uk/news-blog-and-events/press-and-media/innovation-partnership-and-data-can-help-improve-student-mental-health-in-new-14m-drive.

Weale, S & Baldwin, J 2021 ‘Edinburgh University admits failings after student kills herself’, The Guardian, 16th March, https://www.theguardian.com/education/2021/mar/16/edinburgh-university-admits-failings-after-student-kills-herself-internal-review-support-mental-health.

Williamson, B 2017 ‘Digital education governance: political analytics, performativity and accountability’, in Big data in education: The digital future of learning, policy and practice, Sage.


Block: ‘Governing’ with data / Week 9

This week I inventoried the physical objects I use for the Critical Data and Education course (MSCCDE). I wanted to answer the questions:

  • How many physical objects do I use for this course vs how many things do I need?
  • How many resources do I consume through the act of online study?

Originally I wanted to document a physicalisation of these objects instead. I was inspired by Song Dong’s Waste Not installation, and the ways in which materialising data might underline the more-than-human and more-than-digital aspects of human-data-technology assemblages (Lupton 2019). But for space-related reasons I had to stick to a 2D version of this (see Figures 1 & 2).

Then, after reading how imbricated processes of quantification, standardisation and classification transform local knowledge into metrics (Anangnostopoulos et al. 2013), I decided to apply this process to my visualisation just for fun (Context for claim of ‘fun’: I was a library cataloguer before moving to education roles).

I created a 5-minute schema based on my questions and similar specifications I’ve seen used in education e.g. ACARA, CEDS (see Figure 3). I then attempted to standardise and classify my cartoon objects in a way that could be read and analysed by a machine (see Figures 4 & 5).

Figures 1 & 2 and 4 & 5 represent the same objects, and are both mirrors into my way of learning. While the latter figures might prove more useful for answering my questions, thanks to quick classification, the former figures reveal less quantifiable ways in which I relate to and use these objects.

There is a bit of play in each set of figures, represented by the mirror in Figures 2 & 5: I don’t actually use a mirror for study, but for MSCCDE I create ‘data mirrors’ each week through these activities.

It turns out I use a lot of things, many of which I don’t really need but happen to have on hand, and many of which consume power. Next I would try to calculate how much power I actually use. Governing bodies might be interested in data like this for calculating the cost of online learning borne by students. Particularly where carbon consumption shifts away from the campus to households (Filimonau et al. 2021), this data could contribute to more accurate calculations of the carbon costs associated with online education.


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.

Filimonau, V., Archer, D., Bellamy, L., Smith, N. and Wintrip, R., 2020. The carbon footprint of a UK University during the COVID-19 lockdownScience of The Total Environment756, p.143964.

Lupton, D., 2019. Data selves: More-than-human perspectives. John Wiley .