Council on Anthropology and Education
Oral Presentation Session
The increasingly datafied university in the U.S. has introduced substantive changes in how institutions carry out their projects, know students, and map out normative visions for higher education. Extensive data collection, automated through classroom technologies, campus management systems, and even wireless networks, has led to new venues for institutions to change how they address education and their student bodies (Selwyn 2015; Williamson 2017). One of these approaches is predictive analytics, where institutional data scientists anticipate students’ academic performances at the university via predictive modeling.
In this paper I explore how predictive analytics projects are produced alongside student subjectivities and higher education more broadly. Informed by ethnographic research in which I investigated the development of an app conveying predictions to students at a large public university in the United States, I examine the effects of predictive outputs on the shaping of higher education. I draw from my interlocutors’—data scientists, administrators, app developers, and undergraduate students—entanglements with data and predictions to address how predictive projects aim to make student experiences at universities more personalized and productive in line with the neoliberal logics of the institution. I situate predictive projects as a site where central tensions at universities play out: what education is, what role it ought to play in society, and whom it is for.