Abstract—The university student's dropout is a problem that affects the governments, institutions and students. It has negative effects on the high expenditure in the administrative and academic resources. Predicting dropout has become an advantage for university administrators because it allows discovering students that are at risk of dropout as well as develop actions that allow taking decisions in a timely manner. This research presents a neural network approach through the application of multilayer perceptrom algorithms and radial basis function. As input variables to the models, 11 factors were considered, which produce a negative influence in the desertion at the universities; the data was obtained from a survey of 2670 students of a Public University in Ecuador. The results showed that there is no significant difference in the accuracy rates of the proposed models which correspond to 96.3% for multilayer perceptrom and 96.8% for radial basis function. As a conclusion, the studied models could be considered as an optimal option in terms of accuracy and concordance to predict dropout at the universities.
Index Terms—Prediction, university student desertion, neural networks, multilayer perceptrom, radial basis function.
M. Alban, She is with the Faculty of Engineering and Applied Sciences of the Technical University, Cotopaxi (e-mail: mayra.alban@utc.edu.ec). D. Mauricio is with the National University of San Marcos (e-mail: dmauricios@unmsm.edu.pe).
Cite: Mayra Alban and David Mauricio, "Neural Networks to Predict Dropout at the Universities," International Journal of Machine Learning and Computing vol. 9, no. 2, pp. 149-153, 2019.