Early dropout pbkp_rediction model: A case study of university leveling course students


Abstract:

The school-dropout problem is a serious issue that affects both a country’s education system and its economy, given the substantial investment in education made by national governments. One strategy for counteracting the problem at an early stage is to identify students at risk of dropping out. The present study introduces a model to pbkp_redict student dropout rates in the Escuela Politécnica Nacional leveling course. Data related to 2097 higher education students were analyzed; a logistic regression model and an artificial neural network model were trained using four variables, which incorporated student academic and socio-economic information. After comparing the two models, the neural network, with an experimentally defined architecture of 4–7–1 architecture and a logistic activation function, was selected as the model that should be applied to early pbkp_redict dropout in the leveling course. The study findings show that students with the highest risk of dropping out are those in vulnerable situations, with low application grades, from the Costa regime, who are enrolled in the leveling course for technical degrees. This model can be used by the university authorities to identify possible dropout cases, as well as to establish policies to reduce university dropout and failure rates.

Año de publicación:

2020

Keywords:

  • Dropout pbkp_rediction model
  • logistic regression
  • University Students
  • DROPOUT
  • Artificial Neural Network

Fuente:

scopusscopus

Tipo de documento:

Article

Estado:

Acceso abierto

Áreas de conocimiento:

  • Educación superior

Áreas temáticas:

  • Educación superior
  • Educación
  • Escuelas y sus actividades; educación especial