Analysis of First-Year University Student Dropout through Machine Learning Models: A Comparison between Universities

Diego Opazo, Sebastián Moreno, Eduardo Álvarez-Miranda and Jordi Pereira
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Diego Opazo: Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Viña del Mar 2520000, Chile
Sebastián Moreno: Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Viña del Mar 2520000, Chile
Eduardo Álvarez-Miranda: School of Economics and Business, Universidad de Talca, Talca 3460493, Chile
Jordi Pereira: Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Viña del Mar 2520000, Chile

Mathematics, 2021, vol. 9, issue 20, 1-27

Abstract: Student dropout, defined as the abandonment of a high education program before obtaining the degree without reincorporation, is a problem that affects every higher education institution in the world. This study uses machine learning models over two Chilean universities to predict first-year engineering student dropout over enrolled students, and to analyze the variables that affect the probability of dropout. The results show that instead of combining the datasets into a single dataset, it is better to apply a model per university. Moreover, among the eight machine learning models tested over the datasets, gradient-boosting decision trees reports the best model. Further analyses of the interpretative models show that a higher score in almost any entrance university test decreases the probability of dropout, the most important variable being the mathematical test. One exception is the language test, where a higher score increases the probability of dropout.

Keywords: machine learning; first-year student dropout; universities (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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