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dc.contributor.author
Rodríguez Hernández, Carlos Felipe
dc.contributor.author
Musso, Mariel Fernanda
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dc.contributor.author
Kyndt, Eva
dc.contributor.author
Cascallar, Eduardo
dc.date.available
2022-08-29T13:04:25Z
dc.date.issued
2021-03
dc.identifier.citation
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
dc.identifier.uri
http://hdl.handle.net/11336/166796
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ACADEMIC PERFORMANCE
dc.subject
ARTIFICIAL NEURAL NETWORKS
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HIGHER EDUCATION
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PREDICTION
dc.subject.classification
Psicología
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dc.subject.classification
Psicología
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dc.subject.classification
CIENCIAS SOCIALES
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dc.title
Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-07-04T19:55:30Z
dc.identifier.eissn
2666-920X
dc.journal.volume
2
dc.journal.number
100018
dc.journal.pagination
1-14
dc.journal.pais
Estados Unidos
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dc.description.fil
Fil: Rodríguez Hernández, Carlos Felipe. Katholikie Universiteit Leuven; Bélgica
dc.description.fil
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
dc.description.fil
Fil: Kyndt, Eva. Swinburne University Of Technology; Australia. Universiteit Antwerp; Bélgica
dc.description.fil
Fil: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgica
dc.journal.title
Computers and Education: Artificial Intelligence
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.caeai.2021.100018
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2666920X21000126
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