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A data mining approach for predicting academic success – a case study

2019Conference paper Open access
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dc.contributor.authorMartins, Maria Prudência
dc.contributor.authorMiguéis, Vera
dc.contributor.authorFonseca, Davide
dc.contributor.authorAlves, Albano
dc.date.accessioned2020-09-09T15:49:27Z
dc.date.available2020-09-09T15:49:27Z
dc.date.issued2019
dc.description.abstractThe present study puts forward a regression analytic model based on the random forest algorithm, developed to predict, at an early stage, the global academic performance of the undergraduates of a polytechnic higher education institution. The study targets the universe of an institution composed of 5 schools rather than following the usual procedure of delimiting the prediction to one single specific degree course. Hence, we intend to provide the institution with one single tool capable of including the heterogeneity of the universe of students as well as educational dynamics. A different approach to feature selection is proposed, which enables to completely exclude categories of predictive variables, making the model useful for scenarios in which not all categories of data considered are collected. The introduced model can be used at a central level by the decision-makers who are entitled to design actions to mitigate academic failure.pt_PT
dc.description.sponsorshipThis work was supported by the Portuguese Foundation for Science and Technology (FCT) under Project UID/EEA/04131/2013. The authors would also like to thank the Polytechnic Institute of Bragan¸ca for making available the data analysed in this study.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMartins, Maria Prudência; Miguéis, Vera; Fonseca, Davide; Alves, Albano (2019). A data mining approach for predicting academic success – a case study. In Information Technology and Systems: proceedings of ICITS 2019. 918, p. 45-56pt_PT
dc.identifier.doi10.1007/978-3-030-11890-7_5pt_PT
dc.identifier.urihttp://hdl.handle.net/10198/22709
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Nature Switzerland AG 2019pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectData miningpt_PT
dc.subjectEducational data miningpt_PT
dc.subjectAcademic successpt_PT
dc.subjectRandom forestpt_PT
dc.subjectRegressionpt_PT
dc.titleA data mining approach for predicting academic success – a case studypt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F04131%2F2013/PT
oaire.citation.conferencePlaceQuito - Equadorpt_PT
oaire.citation.endPage56pt_PT
oaire.citation.startPage45pt_PT
oaire.citation.titleInformation technology and systems: proceeding of ICITS 2019pt_PT
oaire.citation.volume918pt_PT
oaire.fundingStream5876
person.familyNameMartins
person.familyNameAlves
person.givenNameMaria Prudência
person.givenNameAlbano
person.identifier.ciencia-id4C16-9EE4-B35D
person.identifier.ciencia-id281A-DD4A-2605
person.identifier.orcid0000-0001-9281-7138
person.identifier.orcid0000-0001-9796-6810
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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