Educational Data Mining to Predict Bachelors Students’ Success

Detalhes bibliográficos
Autor(a) principal: Jacob, David
Data de Publicação: 2023
Outros Autores: Henriques, Roberto
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/156451
Resumo: Jacob, D., & Henriques, R. (2023). Educational Data Mining to Predict Bachelors Students’ Success. Emerging Science Journal, 7(Special Issue, "Current Issues, Trends, and New Ideas in Education"), 159-171. https://doi.org/10.28991/ESJ-2023-SIED2-013
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spelling Educational Data Mining to Predict Bachelors Students’ SuccessAcademic SuccessStudent SuccessEducational Data MiningMachine LearningJacob, D., & Henriques, R. (2023). Educational Data Mining to Predict Bachelors Students’ Success. Emerging Science Journal, 7(Special Issue, "Current Issues, Trends, and New Ideas in Education"), 159-171. https://doi.org/10.28991/ESJ-2023-SIED2-013Predicting academic success is essential in higher education because it is perceived as a critical driver for scientific and technological advancement and countries’ economic and social development. This paper aims to retrieve the most relevant attributes for academic success by applying educational data mining (EDM) techniques to a Portuguese business school bachelor’s historical data. We propose two predictive models to classify each student regarding academic success at enrolment and the end of the first academic year. We implemented a SEMMA methodology and tried several machine learning algorithms, including decision trees, KNN, neural networks, and SVM. The best classifier for academic success at the entry-level reached is a random forest with an accuracy of 69%. At the end of the first academic year, an MLP artificial neural network’s best performance was achieved with an accuracy of 85%. The main findings show that at enrolment or the end of the first year, the grades and, thus, the student’s previous education and engagement with the school environment are decisive in achieving academic success.NOVA School of Business and Economics (NOVA SBE)NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNJacob, DavidHenriques, Roberto2023-08-09T22:18:55Z2023-07-272023-07-27T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article13application/pdfhttp://hdl.handle.net/10362/156451eng2610-9182PURE: 68437162https://doi.org/10.28991/ESJ-2023-SIED2-013info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-08-21T01:30:56ZPortal AgregadorONG
dc.title.none.fl_str_mv Educational Data Mining to Predict Bachelors Students’ Success
title Educational Data Mining to Predict Bachelors Students’ Success
spellingShingle Educational Data Mining to Predict Bachelors Students’ Success
Jacob, David
Academic Success
Student Success
Educational Data Mining
Machine Learning
title_short Educational Data Mining to Predict Bachelors Students’ Success
title_full Educational Data Mining to Predict Bachelors Students’ Success
title_fullStr Educational Data Mining to Predict Bachelors Students’ Success
title_full_unstemmed Educational Data Mining to Predict Bachelors Students’ Success
title_sort Educational Data Mining to Predict Bachelors Students’ Success
author Jacob, David
author_facet Jacob, David
Henriques, Roberto
author_role author
author2 Henriques, Roberto
author2_role author
dc.contributor.none.fl_str_mv NOVA School of Business and Economics (NOVA SBE)
NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Jacob, David
Henriques, Roberto
dc.subject.por.fl_str_mv Academic Success
Student Success
Educational Data Mining
Machine Learning
topic Academic Success
Student Success
Educational Data Mining
Machine Learning
description Jacob, D., & Henriques, R. (2023). Educational Data Mining to Predict Bachelors Students’ Success. Emerging Science Journal, 7(Special Issue, "Current Issues, Trends, and New Ideas in Education"), 159-171. https://doi.org/10.28991/ESJ-2023-SIED2-013
publishDate 2023
dc.date.none.fl_str_mv 2023-08-09T22:18:55Z
2023-07-27
2023-07-27T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/156451
url http://hdl.handle.net/10362/156451
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2610-9182
PURE: 68437162
https://doi.org/10.28991/ESJ-2023-SIED2-013
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 13
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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repository.mail.fl_str_mv
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