Purdue University Graduate School

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Bodies of Data: The Social Production of Predictive Analytics

posted on 2020-06-26, 15:56 authored by Madisson WhitmanMadisson Whitman
Bodies of Data challenges the promise of big data in knowing and organizing people by explicating how data are made and theorizing mismatches between actors, data, and institutions. Situated at a large public university in the United States that hosts approximately 30,000 undergraduate students, this research ethnographically traces the development and deployment of an app for student success that draws from traditional (demographic information, enrollment history, grade distributions) and non-traditional (WiFi network usage, card swipes, learning management systems) student data to anticipate the likelihood of graduation in a four-year period. The app, which offers an interface for students based on nudging, is the product of collaborations between actors who specialize in educational technology. As these actors manage the app, they must also interpret data against the students who generate those data, many of whom do not neatly mirror their data counterparts. The central question animating this research asks how the designers of the app create order—whether through material bodies that are knowable to data collection or reorganized demographic groupings—as they render students into data.

To address this question and investigate practices of making data, I conducted 12 months of ethnographic fieldwork, using participant observation and interviewing with university administrators, data scientists, app developers, and undergraduate students. Through a theoretical approach informed by anthropology, science and technology studies, critical data studies, and feminist theory, I analyze how data and the institution make each other through the modeling of student bodies and reshaping of subjectivity. I leverage technical glitches—slippages between students and their data—and failure at large at the institution as analytics to both expose otherwise hidden processes of ordering and productively read failure as an opportunity for imagining what data could do. Predictive projects that derive from big data are increasingly common in higher education as institutions look to data to understand populations. Bodies of Data empirically provides evidence regarding how data are made through sociotechnical processes, in which data are not for understanding but for ordering. As universities look to big data to inform decision-making, the findings of this research contradict assumptions that data provide neutral and objective ways of knowing students.


Purdue Research Foundation Research Grant


Degree Type

  • Doctor of Philosophy


  • Anthropology

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Evelyn Blackwood

Advisor/Supervisor/Committee co-chair

Zoe Nyssa

Additional Committee Member 2

Sherylyn Briller

Additional Committee Member 3

Kendall Roark

Additional Committee Member 4

Laura Zanotti