Set amid intensive data collection and predictive analytics efforts at a large public university in the U.S., this paper explores how undergraduate students become subject to technological interventions aimed at improving graduation and retention rates. The paper posits that these interventions are productive of an ideal subject of predictive analytics who learns to embody predictive outputs and interpretations of their data. This subject is not a model student but a modeled student, a standard of what students could be if they act upon predictions and data to improve their academic performance. However, as the paper discusses, this modeled student becomes a problematic standard that minimizes students’ constraints and circumstances. This paper is a draft chapter from a larger book manuscript project.
The history of science workshop provides historians of science in the New York City area with a collegial, informal environment to share works-in-progress. Presenters circulate a paper a week in advance and discuss it with workshop attendees.
Madi Whitman, Postdoctoral Research Scholar and Assistant Director of Co-teaching at the Center for Science and Society at Columbia University
This event is free and open to the public. To register and receive a copy of a paper, please email Sean O'Neil at [email protected].
This event is part of the New York History of Science Lecture Series.
- The University Seminars at Columbia University
- Columbia University in the City of New York
- NYU Gallatin School of Individualized Study
- The Graduate Center, City University of New York
- The New York Academy of Medicine
- The New York Academy of Sciences
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