Even though I had a really busy week at work and wasn’t able to participate in the Tweetorial, I monitored the tweets regularly and tried to visualise them. I wanted to illustrate the richness of the discussion so I chose to pull out the main terms from the various tweets.
Looking at the visualisation, key themes and issues become clear even for those who aren’t familiar with any of the literature on ‘governing’ with data.
As so often when data are involved, the terms ‘objective’ and ‘impartial’ appear. In the same breath, however, ‘bias’ and ‘subjectivity’ are mentioned, reminding us that data aren’t neutral. When it comes to data and governing, ‘accountability’ is a term that frequently arose in both tweets and literature. Big data and the associated notions of countability, numbering and statistical knowledge, give rise to new forms of ‘data-driven governance’ with an emphasis on ‘evidence-based policymaking’ (Williamson, 2017). This shift raises questions of whether bias is considered in policy making and which actors are involved.
An example of how powerful global policy actors have become is the OECD. Outcomes of their Programme for International Student Assessment (PISA) can result in countries changing their education policies in order to perform better in the assessment (Liss, 2013). Germany was one of the countries that performed badly in 2001 and the resulting ‘PISA shock’ led to steps being taken to improve test results. While this was achieved, the inequality gap in Germany has widened (Davoli and Entorf, 2018), reminding us of the potential issues of standardisation in education.
References
Davoli, M. & Entorf, H. (2018) ‘The PISA Shock, Socioeconomic Inequality, and School Reforms in Germany’, IZA Policy Papers 140, Institute of Labor Economics (IZA).
Liss, J. (2013) ‘Creative destruction and globalization: The rise of massive standardized education platforms’, Globalizations, Vol. 10, No. 4, pp. 557-570.
Williamson, B. (2017). Big Data in Education: The digital future of learning, policy and practice. Sage.
Visualizing the tweetorial was a fabulous idea. And you’ve shown how simple it can be to identify patterns from the data. I suppose the invisible aspect of the visualization is the questions that were asked to stimulate these responses. These data were elicited for a specific purpose, as data almost always are, and are therefore highly partial – despite claims of their impartiality.
Bringing in the OECD to your discussion is useful. Do try to find time to explore the OECD GPS as a suggested task for this week. As you’ve rightly noted, the OECD is extremely powerful in shaping education policy internationally. Some might argue that OECD in fact ‘governs’ the ways national and local policies are formed – as the German example indicates so clearly. There is a kind of global ‘infrastructure’ of testing and analysis technologies and people required for OECD assessments. Do think through further about the implications of this – as you’ve already noted, internationally standardized models of education may be one implication. But – as I think your German example indicates – the local effects may not be so straightforward.