A Model for Predicting Dropout of Higher Education Students

Abstract

Higher education institutions are becoming increasingly concerned with the retention of their students. This work is motivated by the interest in predicting and reducing student dropout, and consequently in reducing the financial loss of said institutions. From the characterization of the dropout problem, and application of a knowledge discovery process, a model (ensemble) to improve dropout prediction is proposed. The ensemble model combines the results of three models: Logistic Regression, Neural Networks, and Decision Tree. As a result, the model can correctly classify 89% of the students (as enrolled and dropped) and accurately identify 98.1% dropouts. When the proposed model is compared with the ensemble method, Random Forest, the proposed model presented desirable characteristics to assist the management in proposing actions to retain students.