TY - JOUR

T1 - Analysis of first-year university student dropout through machine learning models

T2 - A comparison between universities

AU - Opazo, Diego

AU - Moreno, Sebastián

AU - Álvarez-Miranda, Eduardo

AU - Pereira, Jordi

N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2021/10/2

Y1 - 2021/10/2

N2 - 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.

AB - 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.

KW - First-year student dropout

KW - Machine learning

KW - Universities

UR - http://www.scopus.com/inward/record.url?scp=85117456786&partnerID=8YFLogxK

U2 - 10.3390/math9202599

DO - 10.3390/math9202599

M3 - Article

AN - SCOPUS:85117456786

SN - 2227-7390

VL - 9

JO - Mathematics

JF - Mathematics

IS - 20

M1 - 2599

ER -