The Data Revolution Comes to Higher Education: Identifying Students at Risk of Dropout in Chile

23 Pages Posted: 27 Nov 2017 Last revised: 26 Jan 2020

See all articles by Paul von Hippel

Paul von Hippel

University of Texas at Austin - Lyndon B. Johnson School of Public Affairs

Alvaro Hofflinger

Universidad de la Frontera

Date Written: November 19, 2017

Abstract

Enrolment 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.

Suggested Citation

von Hippel, Paul and Hofflinger, Alvaro, The Data Revolution Comes to Higher Education: Identifying Students at Risk of Dropout in Chile (November 19, 2017). Available at SSRN: https://ssrn.com/abstract=3073912 or http://dx.doi.org/10.2139/ssrn.3073912

Paul Von Hippel (Contact Author)

University of Texas at Austin - Lyndon B. Johnson School of Public Affairs ( email )

2315 Red River, Box Y
Austin, TX 78712
United States

Alvaro Hofflinger

Universidad de la Frontera ( email )

Temuco
Chile

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