Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation

Autores
Rodríguez Hernández, Carlos Felipe; Musso, Mariel Fernanda; Kyndt, Eva; Cascallar, Eduardo
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The applications of artificial intelligence in education have increased in recent years. However, further conceptual and methodological understanding is needed to advance the systematic implementation of these approaches. The first objective of this study is to test a systematic procedure for implementing artificial neural networks to predict academic performance in higher education. The second objective is to analyze the importance of several well-known predictors of academic performance in higher education. The sample included 162,030 students of both genders from private and public universities in Colombia. The findings suggest that it is possible to systematically implement artificial neural networks to classify students’ academic performance as either high (accuracy of 82%) or low (accuracy of 71%). Artificial neural networks outperform other machine-learning algorithms in evaluation metrics such as the recall and the F1 score. Furthermore, it is found that prior academic achievement, socioeconomic conditions, and high school characteristics are important predictors of students’ academic performance in higher education. Finally, this study discusses recommendations for implementing artificial neural networks and several considerations for the analysis of academic performance in higher education.
Fil: Rodríguez Hernández, Carlos Felipe. Katholikie Universiteit Leuven; Bélgica
Fil: Musso, Mariel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; Argentina
Fil: Kyndt, Eva. Swinburne University Of Technology; Australia. Universiteit Antwerp; Bélgica
Fil: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgica
Materia
ACADEMIC PERFORMANCE
ARTIFICIAL NEURAL NETWORKS
HIGHER EDUCATION
PREDICTION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/166796
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluationRodríguez Hernández, Carlos FelipeMusso, Mariel FernandaKyndt, EvaCascallar, EduardoACADEMIC PERFORMANCEARTIFICIAL NEURAL NETWORKSHIGHER EDUCATIONPREDICTIONhttps://purl.org/becyt/ford/5.1https://purl.org/becyt/ford/5The applications of artificial intelligence in education have increased in recent years. However, further conceptual and methodological understanding is needed to advance the systematic implementation of these approaches. The first objective of this study is to test a systematic procedure for implementing artificial neural networks to predict academic performance in higher education. The second objective is to analyze the importance of several well-known predictors of academic performance in higher education. The sample included 162,030 students of both genders from private and public universities in Colombia. The findings suggest that it is possible to systematically implement artificial neural networks to classify students’ academic performance as either high (accuracy of 82%) or low (accuracy of 71%). Artificial neural networks outperform other machine-learning algorithms in evaluation metrics such as the recall and the F1 score. Furthermore, it is found that prior academic achievement, socioeconomic conditions, and high school characteristics are important predictors of students’ academic performance in higher education. Finally, this study discusses recommendations for implementing artificial neural networks and several considerations for the analysis of academic performance in higher education.Fil: Rodríguez Hernández, Carlos Felipe. Katholikie Universiteit Leuven; BélgicaFil: Musso, Mariel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; ArgentinaFil: Kyndt, Eva. Swinburne University Of Technology; Australia. Universiteit Antwerp; BélgicaFil: Cascallar, Eduardo. Katholikie Universiteit Leuven; BélgicaElsevier2021-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/166796Rodríguez Hernández, Carlos Felipe; Musso, Mariel Fernanda; Kyndt, Eva; Cascallar, Eduardo; Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation; Elsevier; Computers and Education: Artificial Intelligence; 2; 100018; 3-2021; 1-142666-920XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.caeai.2021.100018info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2666920X21000126info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:13:24Zoai:ri.conicet.gov.ar:11336/166796instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:13:24.871CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation
title Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation
spellingShingle Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation
Rodríguez Hernández, Carlos Felipe
ACADEMIC PERFORMANCE
ARTIFICIAL NEURAL NETWORKS
HIGHER EDUCATION
PREDICTION
title_short Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation
title_full Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation
title_fullStr Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation
title_full_unstemmed Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation
title_sort Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation
dc.creator.none.fl_str_mv Rodríguez Hernández, Carlos Felipe
Musso, Mariel Fernanda
Kyndt, Eva
Cascallar, Eduardo
author Rodríguez Hernández, Carlos Felipe
author_facet Rodríguez Hernández, Carlos Felipe
Musso, Mariel Fernanda
Kyndt, Eva
Cascallar, Eduardo
author_role author
author2 Musso, Mariel Fernanda
Kyndt, Eva
Cascallar, Eduardo
author2_role author
author
author
dc.subject.none.fl_str_mv ACADEMIC PERFORMANCE
ARTIFICIAL NEURAL NETWORKS
HIGHER EDUCATION
PREDICTION
topic ACADEMIC PERFORMANCE
ARTIFICIAL NEURAL NETWORKS
HIGHER EDUCATION
PREDICTION
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.1
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv The applications of artificial intelligence in education have increased in recent years. However, further conceptual and methodological understanding is needed to advance the systematic implementation of these approaches. The first objective of this study is to test a systematic procedure for implementing artificial neural networks to predict academic performance in higher education. The second objective is to analyze the importance of several well-known predictors of academic performance in higher education. The sample included 162,030 students of both genders from private and public universities in Colombia. The findings suggest that it is possible to systematically implement artificial neural networks to classify students’ academic performance as either high (accuracy of 82%) or low (accuracy of 71%). Artificial neural networks outperform other machine-learning algorithms in evaluation metrics such as the recall and the F1 score. Furthermore, it is found that prior academic achievement, socioeconomic conditions, and high school characteristics are important predictors of students’ academic performance in higher education. Finally, this study discusses recommendations for implementing artificial neural networks and several considerations for the analysis of academic performance in higher education.
Fil: Rodríguez Hernández, Carlos Felipe. Katholikie Universiteit Leuven; Bélgica
Fil: Musso, Mariel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; Argentina
Fil: Kyndt, Eva. Swinburne University Of Technology; Australia. Universiteit Antwerp; Bélgica
Fil: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgica
description The applications of artificial intelligence in education have increased in recent years. However, further conceptual and methodological understanding is needed to advance the systematic implementation of these approaches. The first objective of this study is to test a systematic procedure for implementing artificial neural networks to predict academic performance in higher education. The second objective is to analyze the importance of several well-known predictors of academic performance in higher education. The sample included 162,030 students of both genders from private and public universities in Colombia. The findings suggest that it is possible to systematically implement artificial neural networks to classify students’ academic performance as either high (accuracy of 82%) or low (accuracy of 71%). Artificial neural networks outperform other machine-learning algorithms in evaluation metrics such as the recall and the F1 score. Furthermore, it is found that prior academic achievement, socioeconomic conditions, and high school characteristics are important predictors of students’ academic performance in higher education. Finally, this study discusses recommendations for implementing artificial neural networks and several considerations for the analysis of academic performance in higher education.
publishDate 2021
dc.date.none.fl_str_mv 2021-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/166796
Rodríguez Hernández, Carlos Felipe; Musso, Mariel Fernanda; Kyndt, Eva; Cascallar, Eduardo; Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation; Elsevier; Computers and Education: Artificial Intelligence; 2; 100018; 3-2021; 1-14
2666-920X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/166796
identifier_str_mv Rodríguez Hernández, Carlos Felipe; Musso, Mariel Fernanda; Kyndt, Eva; Cascallar, Eduardo; Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation; Elsevier; Computers and Education: Artificial Intelligence; 2; 100018; 3-2021; 1-14
2666-920X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.caeai.2021.100018
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2666920X21000126
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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