Analysis of first-year university student dropout through machine learning models: A comparison between universities

Diego Opazo, Sebastián Moreno, Eduardo Álvarez-Miranda, Jordi Pereira

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

16 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Número de artículo2599
PublicaciónMathematics
Volumen9
N.º20
DOI
EstadoPublicada - 2 oct. 2021
Publicado de forma externa

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