Best Practices, Technologies of Self, and Technics Before and After Big Data

• In Event: 18.017 - The Unintended Consequences of Historical "Best Practices" in "Post-Truth" Times: A National and International Perspective

Abstract

Best practices are sometimes posited as best through an effort to decontextualize cultural differences in teaching-learning or to override them. This can entail efforts to psychologize individuals as having the same cognitive system worldwide, what Stiegler (2015; 2014) calls the rise of “dissociated milieus,” or it can entail efforts to account for cultural differences through numerically coding beliefs, emotions, and behaviors that are then internalized as “identity,” what Foucault (2004; 1984) calls “technologies of self.” The former orientation to best practices as psychologized inscribes cognition and individuality within a long-standing Cartesian mind-body dialectic. The latter treats cultural variation as real but as ultimately observable and numerical, positioning cultural differences as stable entities that can be factored into policy and distributive logics precisely and without remainder.

A noticeable locus for both strategies of truth-production currently is Big Data. What makes Big Data big, enabling cross-platform mining and patterning are the three V’s of volume, velocity and variability, which present ethical, legal, and epistemological challenges in a range of educational realms, from teacher hiring and firing to the microphysics of one-on-one classroom interaction (O’Neil, 2016). There has been a concomitant upsurge in critical questioning of Big Data’s two main variants in education, Educational Data Mining and Learning Analytics. Critiques entail consideration of the purposes of schooling and 21st century competencies, what can and cannot constitute data on that basis, the use, abuse, and misuse of different kinds, who or what gets to control and “own” data, the (in)accuracies of predictive analytics, and the (over)use of algorithms and numerical notation as representation (Buckingham Shum & Deakin Crick, 2016; Enyon, 2013; Koro-Ljungberg & Maclure, 2013).

While the emergence and critique of Big Data in education resembles the fallout that many new movements experience, there has been less reflection on more onto-epistemological issues that relate to the specificity of Big Data technics and technologies of self. As such, this paper reapproaches Big Data’s meeting with education from alternate curriculum studies perspectives. It draws into conversation the work of Bernard Stiegler (technics) and Michel Foucault (technologies of self), elaborating how each can differently position our understanding of the politics of Big Data in educational systems and the implications of this analytical leverage for debate over best practices.

The first section examines the definitional debates and etymological history of Big Data, as well as major differences between Educational Data Mining and Learning Analytics as described within educational research. The literature on Big Data in different disciplines and educational research’s specific uptake thus constitute the “data set” here examined. Second, Foucault’s technologies of self and Stiegler’s technics are put into dialogue to draw a different kind of map around contemporary Big Data debates and the onto-epistemological implications. Third, the paper concludes with consideration of whether Big Data can be understood as a game changer in the educational and curriculum fields and if so, on what basis, with what new ethical and power issues arising, and with what potentially unintended consequences for students, teachers and curriculum.

Author

• Bernadette Baker, University of Wisconsin - Madison