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 education, 13(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.