A data-driven approach to predict first-year students’ academic success in higher education institutions
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/21296 |
Resumo: | This study presents a data mining approach to predict academic success of the first-year students. A dataset of 10 academic years for first-year bachelor’s degrees from a Portuguese Higher Institution (N = 9652) has been analysed. Features’ selection resulted in a characterising set of 68 features, encompassing socio-demographic, social origin, previous education, special statutes and educational path dimensions. We proposed and tested three distinct course stage data models based on entrance date, end of the first and second curricular semesters. A support vector machines (SVM) model achieved the best overall performance and was selected to conduct a data-based sensitivity analysis. The previous evaluation performance, study gaps and age-related features play a major role in explaining failures at entrance stage. For subsequent stages, current evaluation performance features unveil their predictive power. Suggested guidelines include to provide study support groups to risk profiles and to create monitoring frameworks. From a practical standpoint, a data-driven decision-making framework based on these models can be used to promote academic success. |
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7160 |
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A data-driven approach to predict first-year students’ academic success in higher education institutionsAcademic successData miningHigher educationModellingSVMSensitivity analysisThis study presents a data mining approach to predict academic success of the first-year students. A dataset of 10 academic years for first-year bachelor’s degrees from a Portuguese Higher Institution (N = 9652) has been analysed. Features’ selection resulted in a characterising set of 68 features, encompassing socio-demographic, social origin, previous education, special statutes and educational path dimensions. We proposed and tested three distinct course stage data models based on entrance date, end of the first and second curricular semesters. A support vector machines (SVM) model achieved the best overall performance and was selected to conduct a data-based sensitivity analysis. The previous evaluation performance, study gaps and age-related features play a major role in explaining failures at entrance stage. For subsequent stages, current evaluation performance features unveil their predictive power. Suggested guidelines include to provide study support groups to risk profiles and to create monitoring frameworks. From a practical standpoint, a data-driven decision-making framework based on these models can be used to promote academic success.Springer2021-10-06T00:00:00Z2021-01-01T00:00:00Z20212021-05-04T12:07:06Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/21296eng1360-235710.1007/s10639-020-10346-6Gil, P. D.Martins, S. C.Moro, S.Costa, J. M.info: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:RCAAP2023-11-09T17:27:40Zoai:repositorio.iscte-iul.pt:10071/21296Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:12:20.439005Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A data-driven approach to predict first-year students’ academic success in higher education institutions |
title |
A data-driven approach to predict first-year students’ academic success in higher education institutions |
spellingShingle |
A data-driven approach to predict first-year students’ academic success in higher education institutions Gil, P. D. Academic success Data mining Higher education Modelling SVM Sensitivity analysis |
title_short |
A data-driven approach to predict first-year students’ academic success in higher education institutions |
title_full |
A data-driven approach to predict first-year students’ academic success in higher education institutions |
title_fullStr |
A data-driven approach to predict first-year students’ academic success in higher education institutions |
title_full_unstemmed |
A data-driven approach to predict first-year students’ academic success in higher education institutions |
title_sort |
A data-driven approach to predict first-year students’ academic success in higher education institutions |
author |
Gil, P. D. |
author_facet |
Gil, P. D. Martins, S. C. Moro, S. Costa, J. M. |
author_role |
author |
author2 |
Martins, S. C. Moro, S. Costa, J. M. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Gil, P. D. Martins, S. C. Moro, S. Costa, J. M. |
dc.subject.por.fl_str_mv |
Academic success Data mining Higher education Modelling SVM Sensitivity analysis |
topic |
Academic success Data mining Higher education Modelling SVM Sensitivity analysis |
description |
This study presents a data mining approach to predict academic success of the first-year students. A dataset of 10 academic years for first-year bachelor’s degrees from a Portuguese Higher Institution (N = 9652) has been analysed. Features’ selection resulted in a characterising set of 68 features, encompassing socio-demographic, social origin, previous education, special statutes and educational path dimensions. We proposed and tested three distinct course stage data models based on entrance date, end of the first and second curricular semesters. A support vector machines (SVM) model achieved the best overall performance and was selected to conduct a data-based sensitivity analysis. The previous evaluation performance, study gaps and age-related features play a major role in explaining failures at entrance stage. For subsequent stages, current evaluation performance features unveil their predictive power. Suggested guidelines include to provide study support groups to risk profiles and to create monitoring frameworks. From a practical standpoint, a data-driven decision-making framework based on these models can be used to promote academic success. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-10-06T00:00:00Z 2021-01-01T00:00:00Z 2021 2021-05-04T12:07:06Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/21296 |
url |
http://hdl.handle.net/10071/21296 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1360-2357 10.1007/s10639-020-10346-6 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799134678698950656 |