Early Dropout Prediction Model: A Case Study of University Leveling Course Students

Iván Sandoval-Palis, David Naranjo, Jack Vidal and Raquel Gilar-Corbi
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Iván Sandoval-Palis: Departamento de Formación Básica, Escuela Politécnica Nacional, Quito 17-01-2759, Ecuador
David Naranjo: Departamento de Formación Básica, Escuela Politécnica Nacional, Quito 17-01-2759, Ecuador
Jack Vidal: Department of Developmental Psychology and Didactics, University of Alicante, 03690 Alicante, Spain
Raquel Gilar-Corbi: Department of Developmental Psychology and Didactics, University of Alicante, 03690 Alicante, Spain

Sustainability, 2020, vol. 12, issue 22, 1-17

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 predict 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 predict 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.

Keywords: dropout; artificial neural network; logistic regression; dropout prediction model; university students (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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