Neural Network Model for Predicting Student Failure in the Academic Leveling Course of Escuela Politécnica Nacional
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Title: | Neural Network Model for Predicting Student Failure in the Academic Leveling Course of Escuela Politécnica Nacional |
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Authors: | Sandoval-Palis, Iván | Naranjo, David | Gilar-Corbi, Raquel | Pozo-Rico, Teresa |
Research Group/s: | Investigación en Inteligencias, Competencia Social y Educación (SOCEDU) | Habilidades, Competencias e Instrucción |
Center, Department or Service: | Universidad de Alicante. Departamento de Psicología Evolutiva y Didáctica |
Keywords: | Neural network | Predictive modeling | Student success | Academic leveling course | Learning analytics | Academic performance |
Knowledge Area: | Psicología Evolutiva y de la Educación |
Issue Date: | 9-Dec-2020 |
Publisher: | Frontiers Media |
Citation: | Sandoval-Palis I, Naranjo D, Gilar-Corbi R and Pozo-Rico T (2020) Neural Network Model for Predicting Student Failure in the Academic Leveling Course of Escuela Politécnica Nacional. Front. Psychol. 11:515531. doi: 10.3389/fpsyg.2020.515531 |
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. |
Sponsor: | This research was supported by Escuela Politécnica Nacional. |
URI: | http://hdl.handle.net/10045/110802 |
ISSN: | 1664-1078 |
DOI: | 10.3389/fpsyg.2020.515531 |
Language: | eng |
Type: | info:eu-repo/semantics/article |
Rights: | © 2020 Sandoval-Palis, Naranjo, Gilar-Corbi and Pozo-Rico. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Peer Review: | si |
Publisher version: | https://doi.org/10.3389/fpsyg.2020.515531 |
Appears in Collections: | INV - SOCEDU - Artículos de Revistas INV - Habilidades, Competencias e Instrucción - Artículos de Revistas |
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Sandoval-Palis_etal_2020_FrontPsychol.pdf | 524,84 kB | Adobe PDF | Open Preview | |
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