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The data revolution comes to higher education: Identifying students at risk of dropout in Chile

Enrollment in higher education has risen dramatically in Latin America, especially in Chile. Yet graduation and persistence rates remain low. One way to improve graduation and persistence is to use data and analytics to identify students at risk of dropout, target interventions, and evaluate interventions’ effectiveness at improving student success. We illustrate the potential of this approach using data from eight Chilean universities. Results show that data available at matriculation are only weakly predictive of persistence, while prediction improves dramatically once data on university grades become available. Some predictors of persistence are under policy control. Financial aid predicts higher persistence, and being denied a first-choice major predicts lower persistence. Student success programs are ineffective at some universities; they are more effective at others, but when effective they often fail to target the highest risk students. Universities should use data regularly and systematically to identify high-risk students, target them with interventions, and evaluate those interventions’ effectiveness.

Keywords
predictive analytics
Education level
Document Object Identifier (DOI)
10.26300/2qfx-tq33

This EdWorkingPaper is published in:

von Hippel, P.T. & Hofflinger, A.Q. (2020). The data revolution comes to higher education: Identifying students at risk of dropout in Chile. Journal of Higher Education Policy and Management. https://doi.org/10.1080/1360080X.2020.1739800

EdWorkingPaper suggested citation:

von Hippel, Paul T., and Alvaro Hofflinger . (). The data revolution comes to higher education: Identifying students at risk of dropout in Chile. (EdWorkingPaper: -249). Retrieved from Annenberg Institute at Brown University: https://doi.org/10.26300/2qfx-tq33

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