University Dropout: A Prediction Model for an Engineering Program in Bogotá, Colombia

In Colombia, the desertion average rate shows that only around half of the students that begin an undergraduate program are finishing their studies. Although several models have been developed, the results of the implementation of strategies for student retention have not been sufficiently effective. Therefore, this study focuses on the creation of robust predictive models that allow timely anticipation of the risk that an engineering student will retire prematurely from the program. This phenomenon is analyzed in an empirical way through a methodology of Knowledge Discovery in Databases (KDD) using different machine learning techniques. Results show that the academic cost (in terms of the number of subjects viewed) and the semester the student entered have a significant impact on the probability of dropout occurring, especially when considering the dropout in the firsts semester. Then, acting on the students predicted by the model might reduce the number of dropouts.