Using Predictive Analytics to Track Students: Evidence from a Seven-College Experiment
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.
The research reported here was undertaken through the Center for the Analysis of Postsecondary Readiness and supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305C140007 to Teachers College, Columbia University. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education. Numerous people—including several from the Community College Research Center at Teachers College, Columbia University—have helped make this work happen. We particularly thank Elisabeth Barnett for her leadership, as well as Clive Belfield, Magdalena Bennett, Dan Cullinan, Vikash Reddy, and Susha Roy for their contributions. We also thank Judy Scott-Clayton for her comments and advice. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.