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

13 Pages Posted: 27 Nov 2017 Last revised: 14 Jan 2018

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

Higher education has begun to use data and analytics to identify students at risk of dropout and to target and evaluate interventions. In this article, we present initial results from a model that uses data to predict student persistence at a Chilean university. The information available when students begin college predicts success poorly, and high school grades are more predictive than test scores or family background. Data from the first semester of college improves prediction dramatically. Data from the second semester improves prediction only a little more, and many students have already dropped out by the time second semester data are available. Some predictive variables suggest mechanisms for intervention, such as optimizing the distribution of fellowships and allowing more students into their first choice major. Colleges and universities should use first semester data to identify at-risk students and initiate targeted prevention.

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

Register to save articles to
your library

Register

Paper statistics

Downloads
62
rank
336,929
Abstract Views
268
PlumX Metrics