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

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.

Automated summary

The study found that student dropout is a global issue, and research on two Chilean universities suggests that machine learning models should be applied separately for each university when predicting first-year engineering student dropout rates. The results show that a higher score in almost any entrance university test decreases the probability of dropout, with the mathematical test being the most important variable, while the language test score increases the probability of dropout.