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.
|Number of pages||5|
|Journal||International Journal of Machine Learning and Computing|
|State||Published - 1 Apr 2019|
Bibliographical noteFunding Information:
Manuscript received October 20, 2018; revised March 1, 2019. This work was supported in part by Technical University of Cotopaxi, Faculty of Computer Science and Computer Systems. Av. Simón Rodriguez, Latacunga, Ecuador, and National University of San Marcos, Group of Artificial Intelligence, Faculty of Computer Systems. Av. German Aemzaga 375, Lima1, Lima, Perú.
© 2019, International Association of Computer Science and Information Technology.
Copyright 2019 Elsevier B.V., All rights reserved.
- Multilayer perceptrom
- Neural networks
- Radial basis function
- University student desertion