TY  - JOUR
AB  - 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.
AU  - Hippel, Paul T. von
AU  - Hofflinger, Alvaro
DA  - July 2020
DO  - 10.26300/2qfx-tq33
PY  - 2020
ST  - The data revolution comes to higher education: Identifying students at risk of dropout in Chile
T2  - EdWorkingPapers.com
TI  - The data revolution comes to higher education: Identifying students at risk of dropout in Chile
UR  - https://www.edworkingpapers.com/ai20-249
ID  - 223
ER  -