Skip navigation
User training | Reference and search service

Library catalog

EDS
b-on
More
resources
Content aggregators
Please use this identifier to cite or link to this item:

acessibilidade

http://hdl.handle.net/10071/21296
Full metadata record
acessibilidade
DC FieldValueLanguage
dc.contributor.authorGil, P. D.-
dc.contributor.authorMartins, S. C.-
dc.contributor.authorMoro, S.-
dc.contributor.authorCosta, J. M.-
dc.date.accessioned2021-01-15T14:10:01Z-
dc.date.issued2021-
dc.identifier.issn1360-2357-
dc.identifier.urihttp://hdl.handle.net/10071/21296-
dc.description.abstractThis 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.isoeng-
dc.publisherSpringer-
dc.relationUIDB/04466/2020-
dc.relationUIDP/04466/2020-
dc.rightsembargoedAccess-
dc.subjectAcademic successeng
dc.subjectData miningeng
dc.subjectHigher educationeng
dc.subjectModellingeng
dc.subjectSVMeng
dc.subjectSensitivity analysiseng
dc.titleA data-driven approach to predict first-year students’ academic success in higher education institutionseng
dc.typearticle-
dc.peerreviewedyes-
dc.journalEducation and Information Technologies-
dc.volumeN/A-
degois.publication.titleA data-driven approach to predict first-year students’ academic success in higher education institutionseng
dc.date.updated2021-03-16T14:55:05Z-
dc.description.versioninfo:eu-repo/semantics/acceptedVersion-
dc.identifier.doi10.1007/s10639-020-10346-6-
dc.subject.fosDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informaçãopor
dc.subject.fosDomínio/Área Científica::Ciências Sociais::Ciências da Educaçãopor
dc.date.embargo2021-10-06-
iscte.subject.odsEducação de qualidadepor
iscte.identifier.cienciahttps://ciencia.iscte-iul.pt/id/ci-pub-74506-
iscte.alternateIdentifiers.wosWOS:000575713600001-
iscte.alternateIdentifiers.scopus2-s2.0-85092076826-
Appears in Collections:CIES-RI - Artigos em revista científica internacional com arbitragem científica
ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica

Files in This Item:
acessibilidade
File Description SizeFormat 
2020_EducationAndInformationTechnologies-GilMartinsMoroCosta-PosPrint.pdfVersão Aceite645.21 kBAdobe PDFView/Open    Request a copy


FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpace
Formato BibTex MendeleyEndnote Currículo DeGóis 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.