Comparative study of learning analytics techniques to predict student academic performance in higher education

Comparative study of learning analytics techniques to predict student academic performance in higher education

The issue of school dropout involves factors such as students’ engagement that can predict his or her success in school. It has been shown that student engagement has three components: behavioral, emotional and cognitive. Motivation and engagement are strongly related since the former is a precursor...

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Main Authors: Acosta-Gonzaga, Elizabeth, Ramirez-Arellano, Aldo
Format: Online
Language:spa
Published: Universidad Autónoma de Tamaulipas 2020
Online Access:https://revistaciencia.uat.edu.mx/index.php/CienciaUAT/article/view/1392
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id oai:ojs.pkp.sfu.ca:article-1392
record_format ojs
institution CIENCIA UAT
collection OJS
language spa
format Online
author Acosta-Gonzaga, Elizabeth
Ramirez-Arellano, Aldo
spellingShingle Acosta-Gonzaga, Elizabeth
Ramirez-Arellano, Aldo
Comparative study of learning analytics techniques to predict student academic performance in higher education
author_facet Acosta-Gonzaga, Elizabeth
Ramirez-Arellano, Aldo
author_sort Acosta-Gonzaga, Elizabeth
title Comparative study of learning analytics techniques to predict student academic performance in higher education
title_short Comparative study of learning analytics techniques to predict student academic performance in higher education
title_full Comparative study of learning analytics techniques to predict student academic performance in higher education
title_fullStr Comparative study of learning analytics techniques to predict student academic performance in higher education
title_full_unstemmed Comparative study of learning analytics techniques to predict student academic performance in higher education
title_sort comparative study of learning analytics techniques to predict student academic performance in higher education
description The issue of school dropout involves factors such as students’ engagement that can predict his or her success in school. It has been shown that student engagement has three components: behavioral, emotional and cognitive. Motivation and engagement are strongly related since the former is a precursor of engagement. The aim of this study was to compare the efficiency of linear regression against two data mining techniques to predict the students’ academic performance in higher education. A descriptive cross-sectional study was carried out with 222 students from a public higher education institution in Mexico city. An analysis of hierarchical linear regression (LR) and learning analytics techniques such as neural networks (NN) and support vector machine (SVM) was conducted. To assess the accuracy of the learning analytics techniques, an analysis of variance (ANOVA) was carried out. The techniques were compared using cross validation. The results showed that behavioral engagement and self-efficacy had positive effects on student achievements, while passivity showed a negative effect. Likewise, the LR and SVM techniques had the same performance on predicting students’ achievements. The LR has the advantage of producing a simple and easy model. On the contrary, the SVM technique generates a more complex model. Although, if the model were aimed to forecast the performance, the SVM technique would be the most appropriate, since it does not require to verify any statistical assumption.
publisher Universidad Autónoma de Tamaulipas
publishDate 2020
url https://revistaciencia.uat.edu.mx/index.php/CienciaUAT/article/view/1392
work_keys_str_mv AT acostagonzagaelizabeth comparativestudyoflearninganalyticstechniquestopredictstudentacademicperformanceinhighereducation
AT ramirezarellanoaldo comparativestudyoflearninganalyticstechniquestopredictstudentacademicperformanceinhighereducation
AT acostagonzagaelizabeth estudiocomparativodetecnicasdeanaliticadelaprendizajeparapredecirelrendimientoacademicodelosestudiantesdeeducacionsuperior
AT ramirezarellanoaldo estudiocomparativodetecnicasdeanaliticadelaprendizajeparapredecirelrendimientoacademicodelosestudiantesdeeducacionsuperior
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spelling oai:ojs.pkp.sfu.ca:article-13922020-08-01T19:26:43Z Comparative study of learning analytics techniques to predict student academic performance in higher education Estudio comparativo de técnicas de analítica del aprendizaje para predecir el rendimiento académico de los estudiantes de educación superior Acosta-Gonzaga, Elizabeth Ramirez-Arellano, Aldo compromiso del estudiante motivación del estudiante desempeño académico analítica del aprendizaje student engagement student motivation academic achievements learning analytics The issue of school dropout involves factors such as students’ engagement that can predict his or her success in school. It has been shown that student engagement has three components: behavioral, emotional and cognitive. Motivation and engagement are strongly related since the former is a precursor of engagement. The aim of this study was to compare the efficiency of linear regression against two data mining techniques to predict the students’ academic performance in higher education. A descriptive cross-sectional study was carried out with 222 students from a public higher education institution in Mexico city. An analysis of hierarchical linear regression (LR) and learning analytics techniques such as neural networks (NN) and support vector machine (SVM) was conducted. To assess the accuracy of the learning analytics techniques, an analysis of variance (ANOVA) was carried out. The techniques were compared using cross validation. The results showed that behavioral engagement and self-efficacy had positive effects on student achievements, while passivity showed a negative effect. Likewise, the LR and SVM techniques had the same performance on predicting students’ achievements. The LR has the advantage of producing a simple and easy model. On the contrary, the SVM technique generates a more complex model. Although, if the model were aimed to forecast the performance, the SVM technique would be the most appropriate, since it does not require to verify any statistical assumption. La deserción escolar involucra diversos factores, entre ellos, el compromiso del estudiante, a través del cual se puede predecir su éxito en la escuela. Ese compromiso tiene varios componentes, tales como conductual, emocional y cognitivo. La motivación y el compromiso están fuertemente relacionadas, ya que la primera es un precursor del compromiso. El objetivo de este estudio fue comparar la eficacia de la regresión lineal contra dos técnicas de minería de datos para predecir el rendimiento académico de los estudiantes en la educación superior. Se hizo un estudio transversal explicativo en el que se encuestó a 222 estudiantes universitarios de una institución pública de la Ciudad de México. Se realizó un análisis de regresión lineal jerárquico (RL) y de técnicas de analítica del aprendizaje, como redes neuronales (RN) y máquinas de vector soporte (SVM). Para evaluar la exactitud de las técnicas de analítica del aprendizaje se realizó un análisis de varianza (ANOVA). Se compararon las técnicas de analítica del aprendizaje y de regresión lineal usando la validación cruzada. Los resultados mostraron que el compromiso conductual y la autoeficacia tuvieron efectos positivos en el desempeño del estudiante, mientras que la pasividad mostró un efecto negativo. Asimismo, las técnicas de RL y de SVM pronosticaron igualmente el desempeño académico de los estudiantes. La RL tuvo la ventaja de producir un modelo simple y de fácil interpretación. Por el contrario, la técnica de SVM generó un modelo más complejo, aunque, si el modelo tuviese como objetivo el pronóstico del desempeño, la técnica SVM sería la más adecuada, ya que no requiere la verificación de ningún supuesto estadístico. Universidad Autónoma de Tamaulipas 2020-08-01 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/html text/xml https://revistaciencia.uat.edu.mx/index.php/CienciaUAT/article/view/1392 10.29059/cienciauat.v15i1.1392 CienciaUAT; Vol. 15 No. 1. July-December 2020; 63-74 CienciaUAT; Vol. 15 No. 1: Julio-Diciembre 2020; 63-74 2007-7858 2007-7521 spa https://revistaciencia.uat.edu.mx/index.php/CienciaUAT/article/view/1392/747 https://revistaciencia.uat.edu.mx/index.php/CienciaUAT/article/view/1392/763 https://revistaciencia.uat.edu.mx/index.php/CienciaUAT/article/view/1392/780 Derechos de autor 2020 CienciaUAT

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