Analysis of First-Year University Student Dropout through Machine Learning Models: A Comparison between Universities
Opazo
D
author
Moreno
S
author
Alvarez-Miranda
E
author
Pereira
J
author
2021
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.
machine learning
first-year student dropout
universities
exported from refbase (show.php?record=1463), last updated on Fri, 15 Oct 2021 18:33:52 -0300
text
10.3390/math9202599
Opazo_etal2021
Mathematics
Mathematics
2021
continuing
periodical
academic journal
20
9
2599
2227-7390