Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/21296
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Gil, P. D. | - |
dc.contributor.author | Martins, S. C. | - |
dc.contributor.author | Moro, S. | - |
dc.contributor.author | Costa, J. M. | - |
dc.date.accessioned | 2021-01-15T14:10:01Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1360-2357 | - |
dc.identifier.uri | http://hdl.handle.net/10071/21296 | - |
dc.description.abstract | 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. | eng |
dc.language.iso | eng | - |
dc.publisher | Springer | - |
dc.relation | UIDB/04466/2020 | - |
dc.relation | UIDP/04466/2020 | - |
dc.rights | openAccess | - |
dc.subject | Academic success | eng |
dc.subject | Data mining | eng |
dc.subject | Higher education | eng |
dc.subject | Modelling | eng |
dc.subject | SVM | eng |
dc.subject | Sensitivity analysis | eng |
dc.title | A data-driven approach to predict first-year students’ academic success in higher education institutions | eng |
dc.type | article | - |
dc.pagination | 2165 - 2190 | - |
dc.peerreviewed | yes | - |
dc.journal | Education and Information Technologies | - |
dc.volume | 26 | - |
dc.number | 2 | - |
degois.publication.firstPage | 2165 | - |
degois.publication.lastPage | 2190 | - |
degois.publication.issue | 2 | - |
degois.publication.title | A data-driven approach to predict first-year students’ academic success in higher education institutions | eng |
dc.date.updated | 2021-05-04T12:07:06Z | - |
dc.description.version | info:eu-repo/semantics/acceptedVersion | - |
dc.identifier.doi | 10.1007/s10639-020-10346-6 | - |
dc.subject.fos | Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação | por |
dc.subject.fos | Domínio/Área Científica::Ciências Sociais::Ciências da Educação | por |
dc.date.embargo | 2021-10-06 | - |
iscte.subject.ods | Educação de qualidade | por |
iscte.identifier.ciencia | https://ciencia.iscte-iul.pt/id/ci-pub-74506 | - |
iscte.alternateIdentifiers.wos | WOS:000575713600001 | - |
iscte.alternateIdentifiers.scopus | 2-s2.0-85092076826 | - |
Aparece nas coleções: | CIES-RI - Artigos em revistas científicas internacionais com arbitragem científica ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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2020_EducationAndInformationTechnologies-GilMartinsMoroCosta-PosPrint.pdf | Versão Aceite | 645,21 kB | Adobe PDF | Ver/Abrir |
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