The Red Queen: shifting governance and the restructuring of truth

Always speak the truth, think before you speak, and write it down afterwards.

The Red Queen in Alice in Wonderland (Carroll/Tenniel 2009)

Like the Red Queen, education is constantly running without getting anywhere.  The Red Queen herself said you have to run as fast as possible just to stay still.  Since the emergence of education-as-science around 200 years ago it has undergone constant reform (Smith 1998) in search of a more scientific way of doing things.  Arguably this started with Ebbinghaus in 1885 who experimented with reducing learning to small de-contextualised chunks: perfect reductionism (Ibid.).  Today learnification and datafication (Biesta 2012, Knox et. al. 2020) bear the torch for this ongoing improvement of learning.

Datafication affects not just teaching and learning but the governance of teaching and learning (Williamson 2017: 74).  Data is used to support policy and standards in terms of monitoring performance or in researching the justification for those standards (Williamson 2017:74, Ozga 2016, Fontaine 2016).  In my first data visualisation of this blog, I noted the number of standards, compared with other material, in my School’s response to the pivot online.  A huge repository of useful information for academics pivoting online was based largely on standards and policies.  It is interesting to note the standards are based on data collected on best practice in online learning.

As we were to discover, this best practice was shaped by a pervasive narrative in online learning: the social constructivist discourse on Communities of Inquiry (Garrison 2010).  Narratives, once taken to heart, can be quickly reinforced with data.  Our characterisation of data as neutral allows the steady restructuring of new truths from old ones (Anagstopolous 2013:217, Ozga 2016, Fontaine 2016).  This includes dataveillance (Williamson 2017) or data gaze (Prinsloo 2020): collecting data for ostensibly neutral purposes.  However, the data collection priorities often reveal political purposes, subconscious or deliberate (Prinsloo 2020).  A review of recruitment data collected for diversity purposes for my second visualisation revealed a Christian bias.  While these data are unlikely to be used to discriminate; it shows that only certain data are collected and therefore, governance may occur through what is not collected as well as what is.

This restructuring through data is pervasive, undermining old elitist structures and replacing authority through social position with authority through knowledge (Fontaine 2016, Ozga 2016).  But since the privileged tend to have both position and knowledge, this may not reverse society but could drive an even larger wedge through it.  Colonialism is not a metaphor when privileged groups are able to dispossess others (Prinsloo 2020).  In my final visualisation I was able to see from my browsing history which teaching resources I use the most.  If these data were gathered routinely and used to assess performance against a standard, I could quickly find my practice being radically altered to conform to a decontextualised and simplistic narrative, the Ebbinghaus effect.  As approaches to education and technology change, we are all engaged in a Red Queen race to stand still, but data-driven standards and policies could be a step backwards.    


Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. 2013a. 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.

Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. 2013b. Conclusion: The Infrastructure of Accountability: Tensions, Implications and Concluding Thoughts. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds) The Infrastructure of Accountability: Data use and the transformation of American education.

Biesta, G. J. J. (2012). Giving Teaching back to education: responding to the disappearance of the teacher. Phenomenology and Practice, 6(2), 35–49.

Carroll, L., Tenniel, J. (2009). Alice’s Adventures in Wonderland and Through the Looking-Glass. United Kingdom: OUP Oxford.

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.

Garrison, D.R., Anderson, T. and Archer, W., 2010. The first decade of the community of inquiry framework: A retrospective. The internet and higher education13(1-2), pp.5-9.

Knox, J., Williamson, B., & Bayne, S. (2020). Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies. Learning, Media and Technology, 45(1), 31–45.

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

Smith, F., 1998. The book of learning and forgetting. Teachers College Press.

Williamson, B. 2017. Digital Education Governance: political analytics, performativity and accountability. Chapter 4 in Big Data in Education: The digital future of learning, policy and practice. Sage.

Covidian Governance

|Matt Offord

The most intense period of being aware for the need for performativity and accountability I can think of is the recent pivot online response to the shutting down of traditional face to face learning in Higher Education. As an academic in a Business School, there was an intense pressure to achieve what would have been thought previously as impossible, the complete conversion of all courses to an online format. Williamson (2017:74) discusses how policy insturments are applied and linked to performativity and accountability and I recognised immediately the norm of control through managerialism, and performance data. The school responded to the crisis by creating an enourmous heap of resources, generated and curated by staff (myself included), as an online academy for becoming an online teacher. A year later I went back to gather data from the Moodle page where the framework is hosted. I wanted to see how much operational governance was being applied by data.

In all 151 individual items form the framework, in the form of documents, infographics, videos, podcasts or links to external resources. This was a hugely impressive piece of work compiled in just a few weeks by a handful of staff. Inevitably, University, College and School policy formed a part of this. I wanted to discover how many of these items were for collecting data on staff progress in developing these courses. Only 4 items were for this purpose. However, the items are significant, they are reporting forms to collect data on the course build progress. The forms were introduced ostensibly to reduce workload since, technically, academics should have gone through a months-long process to adapt their courses to online. Yet, many academics who would not previously have needed to inform the school of progress preparing for teaching, found that they had to. The other finding is the huge proportion of ‘standards’ that were produced. Given that standards direct effort whereas guides provide optional advice, the framework looks overwhelmingly directively. This was not the intention but increasingly, the School reverted to its customary managerial mode of operation. Standards, although not embedded in the data architecture, are algorithmic in nature (like pre-data algorithms). The response, to me, looks like a panicked attempt to regain control through policies and standards, while the School is yet to be datafied so far…

A Tanglegram of Teaching

|Matt Offord

Humans, things and information are dependent on one another, connected to one another through chains of behaviour (Hodder 2012:54). Data is therefore just one part of a larger assemblage (Brown 2020, Williamson et. al. 2020). Data as separate and valuable without context essentially renders data flat and lifeless just as surely as a fish dies when removed from the ocean.

This week I wished to capture my data architecture (Williamson 2020) and bring it to life as Heidegger’s equipmental totality (Hodder 2012:28). The items on my desk are the architecture of my teaching rather than data specifically. In my teaching, I am not exposed to Learning Analytics Dashboards as described by Brown (2020) and although data on my students slops around the Moodle data lake, I rarely go to the shore, preferring to depend on my interactions. During Covid-19, these items are how I teach (of course they are connected to a wider network of internet pages and servers). I listed the items: phone, webcam, laptop, headphones and tablet. I collected two forms of data: the hours spent on each piece of equipment and how dependent I am on them for teaching.

Dataveillance by Matt Offord 2021

The thingometer is the gauge to the side of each object while the coloured marks show how many hours each is used each day. The laptop, it seems, is crucial for teaching, especially when linked with the webcam and headphones for live teaching. Neither the phone nor tablet are indispensable but provide redundancy for the others. The laptop (with its entangled person, me) can wrangle all the data, qualitative and quantitative and deliver all the teaching necessary. Or to put it another way, it conducts more dataveillance than the other things (Williamson et. al. 2020, Brown 2020)

Brown, M. 2020. Seeing students at scale: how faculty in large lecture courses act upon learning analytics dashboard data. Teaching in Higher Education. 25(4), pp. 384-400

Hodder, I., 2012. Entangled: An archaeology of the relationships between humans and things.

Williamson, B. Bayne, S. Shay, S. 2020. The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education. 25(4), pp. 351-365.