“Have You Achieved Robot Status Yet?”: Predictive Analytics, Universities, and the Making of Student Subjectivities

• In Event: STS and Universities - II

Abstract

Data collection on students within universities in the U.S. has become widespread as institutions seek to know their student bodies and anticipate students’ academic performance (Williamson 2017; Zinshteyn 2016). Some universities use these data, comprised of demographic and enrollment data, grade distributions, and non-traditional data like geolocational information, to algorithmically model and predict the likelihood of students graduating within a four year period and nudge students accordingly.
In this paper, I situate data and algorithmic predictions within a central tension in the landscape of higher education: whom and what universities are for. Informed by ethnographic research in which I investigated the development of an app conveying predictions to students at a large public university, I explore the effects of predictive outputs on the re/making of subjectivities at the institution. I draw from my interlocutors’—data scientists, administrators, app developers, and students—entanglements with data and predictions to address the formation of the algorithmically defined “ideal” student against another finding of expansive data collection: class, race, and gender as major indicators of “success” at the university. Students’ engagements with data-defined standards open up the interplay between the institution and its technologies under expanding neoliberal logics, wherein normative notions of who students are and what their lives ought to look like are increasingly governed through predictive uses of their data.

Author

• Madisson Whitman, Purdue University