Using Predictive Analytics to Track Students: Evidence from a Seven-College Experiment

55 Pages Posted: 26 Jun 2021

See all articles by Peter Bergman

Peter Bergman

Columbia University

Elizabeth Kopko

Columbia University

Julio Rodriguez

Columbia University

Multiple version iconThere are 3 versions of this paper

Abstract

Tracking is widespread in U.S. education. In post-secondary education alone, at least 71% of colleges use a test to track students. However, there are concerns that the most frequently used college placement exams lack validity and reliability, and unnecessarily place students from under-represented groups into remedial courses. While recent research has shown that tracking can have positive effects on student learning, inaccurate placement has consequences: students face misaligned curricula and must pay tuition for remedial courses that do not bear credits toward graduation. We develop an alternative system to place students that uses predictive analytics to combine multiple measures into a placement instrument. Compared to colleges'; existing placement tests, the algorithm is more predictive of future performance. We then conduct an experiment across seven colleges to evaluate the algorithm's effects on students. Placement rates into college-level courses increased substantially without reducing pass rates. Adjusting for multiple testing, algorithmic placement generally, though not always, narrowed gaps in college placement rates and remedial course taking across demographic groups. A detailed cost analysis shows that the algorithmic placement system is socially efficient: it saves costs for students while increasing college credits earned, which more than offsets increased costs for colleges. Costs could be reduced with improved data digitization as opposed to entering data by hand.

JEL Classification: I20, I24

Suggested Citation

Bergman, Peter and Kopko, Elizabeth and Rodriguez, Julio, Using Predictive Analytics to Track Students: Evidence from a Seven-College Experiment. IZA Discussion Paper No. 14500, Available at SSRN: https://ssrn.com/abstract=3874372 or http://dx.doi.org/10.2139/ssrn.3874372

Peter Bergman (Contact Author)

Columbia University ( email )

Elizabeth Kopko

Columbia University

3022 Broadway
New York, NY 10027
United States

Julio Rodriguez

Columbia University

3022 Broadway
New York, NY 10027
United States

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