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. 2020 Dec 9:11:515531.
doi: 10.3389/fpsyg.2020.515531. eCollection 2020.

Neural Network Model for Predicting Student Failure in the Academic Leveling Course of Escuela Politécnica Nacional

Iván Sandoval-Palis  1 , David Naranjo  1 , Raquel Gilar-Corbi  2 , Teresa Pozo-Rico  2
Affiliations

Neural Network Model for Predicting Student Failure in the Academic Leveling Course of Escuela Politécnica Nacional

Iván Sandoval-Palis et al. Front Psychol. .

Abstract

The purpose of this study is to train an artificial neural network model for predicting student failure in the academic leveling course of the Escuela Politécnica Nacional of Ecuador, based on academic and socioeconomic information. For this, 1308 higher education students participated, 69.0% of whom failed the academic leveling course; besides, 93.7% of the students self-identified as mestizo, 83.9% came from the province of Pichincha, and 92.4% belonged to general population. As a first approximation, a neural network model was trained with twelve variables containing students' academic and socioeconomic information. Then, a dimensionality reduction process was performed from which a new neural network was modeled. This dimension reduced model was trained with the variables application score, vulnerability index, regime, gender, and population segment, which were the five variables that explained more than 80% of the first model. The classification accuracy of the dimension reduced model was 0.745, while precision and recall were 0.883 and 0.778, respectively. The area under ROC curve was 0.791. This model could be used as a guide to lead intervention policies so that the failure rate in the academic leveling course would decrease.

Keywords: academic leveling course; academic performance; learning analytics; neural network; predictive modeling; student success.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
(A) ROC curve of neural network 48–39–1. (B) Garson’s weighted importance of the variables of neural network 48–39–1.
FIGURE 2
FIGURE 2
(A) ROC curve of neural network 7–4–1. (B) Garson’s weighted importance of the variables of neural network 7–4–1.
See this image and copyright information in PMC

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