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.
Highlights
Introduction between UniversitiesMathematicsEducation is one of the most important factors in the development of a country.A better and more extensive education helps to improve levels of social wellness and economic growth
In order to compare possible dropout predictors between these universities, we propose multiple machine learning models and compare the dissimilarities among the models learned for each university as well as for a joint dataset covering both universities
Even though true positives are associated with the class with fewer labels, we report the F1 score using both classes as true positive, avoiding misinterpretation of the errors
Summary
Education is one of the most important factors in the development of a country. A better and more extensive education helps to improve levels of social wellness and economic growth. Education decreases social inequalities and promotes social mobility. It helps to build a better society. According to a report from UNESCO in 2015 [1], the global number of students in high education has grown from 28.5 million in 1970 to 196 million in
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