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

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

doi:10.3389/fpsyg.2020.515531

Iván Sandoval-Palis, Raquel Gilar-Corbi + Show 2 more

Open Access

https://doi.org/10.3389/fpsyg.2020.515531

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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.

Highlights

The concern of universities about the quality of the educational service they offer has triggered several and continuous evaluation processes to detect the underlying problems and act in this regard (Sandoval et al, 2019)
Application score, vulnerability index, regime, gender, and population segment were the five variables that showed a relative importance greater than 5%, where the application score and vulnerability index resulted to be the most important for the model
According to the results presented above, a dimension reduced model was trained with the variables application score, vulnerability index, regime, gender, and population segment

Summary

Introduction

The concern of universities about the quality of the educational service they offer has triggered several and continuous evaluation processes to detect the underlying problems and act in this regard (Sandoval et al, 2019). The problems identified through these evaluation processes include several aspects of the education system; one of the most serious is the high rate of student failure in university education, which is significantly higher during the first year of studies. The government and universities have proposed affirmative action policies to help students overcome the difficulties triggered by the influence of these factors. Identifying these factors and analyzing their influence on students’ academic performance is an important process to be performed in order to early identify at-risk students and, implement corrective actions in the educational process (Di Caudo, 2015; Sandoval et al, 2019)

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