Pbkp_rediction of university dropout through technological factors: A case study in Ecuador

Abstract:

Pbkp_redicting dropout in universities has become a concern in several countries around the world. With the introduction of new information and communication technologies, new factors have appeared that influence student dropout in universities. This article proposes an approach to machine learning based on logistic regression techniques and decision trees and factors such as Internet addiction, addiction to social networks and addiction to technology, that affect the desertion of students in universities. As a result, it was obtained that the technique with the highest percentage of dropout precision was decision trees with 91.70%.

Año de publicación:

2018

Keywords:

• Pbkp_rediction of dropout
• Technological factors
• APRENDIZAJE AUTOMÁTICO
• Pbkp_redicción de la deserción
• Machine learning
• FACTORES TECNOLÓGICOS

Fuente:

scopus