Neural networks to pbkp_redict dropout at the universities

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. Pbkp_redicting 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 pbkp_redict dropout at the universities.

Año de publicación:

2019

Keywords:

• Multilayer perceptrom
• pbkp_rediction
• Radial basis function
• Neural networks
• university student desertion

Fuente:

scopus