Prediction of university desertion through hybridization of classification algorithms

At present time, the problem of university desertion in Peru is a social phenomenon that involves loss of Peruvian public investment in higher education (not less than a hundred of millions of dollars per year) and also the investment of their parents. For that reason, the aim of this research is to develop a prediction modeling of the dropout of Peruvian university students that allows us to identify those at greater risk to leave their studies, and giving a possibility to take preventive measures which help to maintain the rate of desertion and in the long term it might be reduced. In relation to the solution, we have identified the most influential factors (twenty-four). Additionally, the methodology used was KDD, and we worked with three classification algorithms: Naive Bayes, Multilayer Perceptron and C4.5 Decision Tree separately, and at the same time forming a hybrid prediction algorithm. Each algorithm has chosen based on its greater frequency of use in diverse researches, and its high precision in the prediction. The case study was the School of Systems Engineering of the National University of San Marcos; we used 840 student data from 2008 to 2013.