Thursday, October 7, 2021 at 10:15-11:45 (UTC+3):
“Synthetic governance: The emergence of new cognitive infrastructures in education policy”
A new synthetic form of educational governance is emerging with the datafication and digitalization of education. Synthetic governance describes the conjunction of: (1) human rationalities, values and practices; (2) data analytics solutions developed by the technology industry in partnership with education institutions and governments; and (3) the algorithmic decision-making, including artificial intelligence, that increasingly drives these solutions. For example, Business Intelligence (BI) systems provide a new cognitive infrastructure for education policy-making based on conjunctive syntheses of human and machine cognition. BI systems combine multiple elements, from data analytics driven by machine learning to data visualization for human users. These systems not only shape thinking about policy problems (what we think), they change the conditions for thought more generally (how we think).
This lecture will report on a case study of an Australian education policy unit (‘The Centre’) that has been implementing a BI strategy to optimize policy-making. I will draw on technical documentation and interview data to analyse the enactment of this strategy and to illustrate how BI and data science methodologies are contributing to the emergence of synthetic governance. The presentation will draw on material from my forthcoming book, Ed Machina: Synthetic Governance, Datafication and Education (University of Minnesota Press), co-authored with Kalervo N. Gulson and P. Taylor Webb.
Suggested reading before the lecture:
Sellar, S. & Gulson, K.N. (2021), Becoming information centric: the emergence of new cognitive infrastructures in education policy. Journal of Education Policy, 36:3, 309-326. https://doi.org/10.1080/02680939.2019.1678766
OR
Gulson, K. N., & Sellar, S. (2019). Emerging data infrastructures and the new topologies of education policy. Environment and Planning D: Society and Space, 37(2), 350–366. https://doi.org/10.1177/0263775818813144