Publicação
A data mining approach for predicting academic success – a case study
| Resumo: | The 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. |
|---|---|
| Autores principais: | Martins, Maria Prudência |
| Outros Autores: | Miguéis, Vera; Fonseca, Davide; Alves, Albano |
| Assunto: | Data mining Educational data mining Academic success Random forest Regression |
| Ano: | 2019 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| _version_ | 1867173026517221376 |
|---|---|
| author | Martins, Maria Prudência |
| author2 | Miguéis, Vera Fonseca, Davide Alves, Albano |
| author2_role | author author author |
| author_facet | Martins, Maria Prudência Miguéis, Vera Fonseca, Davide Alves, Albano |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Martins, Maria Prudência\",\"Person.identifier.orcid\":\"0000-0001-9281-7138\"},{\"Person.name\":\"Miguéis, Vera\"},{\"Person.name\":\"Fonseca, Davide\"},{\"Person.name\":\"Alves, Albano\",\"Person.identifier.orcid\":\"0000-0001-9796-6810\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Martins, Maria Prudência Miguéis, Vera Fonseca, Davide Alves, Albano |
| datacite.date.Accepted.fl_str_mv | 2019-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2020-09-09T15:49:27Z |
| datacite.date.embargoed.fl_str_mv | 2020-09-09T15:49:27Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Data mining Educational data mining Academic success Random forest Regression |
| datacite.titles.title.fl_str_mv | A data mining approach for predicting academic success – a case study |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Martins, Maria Prudência Miguéis, Vera Fonseca, Davide Alves, Albano |
| dc.date.Accepted.fl_str_mv | 2019-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2020-09-09T15:49:27Z |
| dc.date.embargoed.fl_str_mv | 2020-09-09T15:49:27Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/22709 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Springer Nature Switzerland AG 2019 |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Data mining Educational data mining Academic success Random forest Regression |
| dc.title.fl_str_mv | A data mining approach for predicting academic success – a case study |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | The 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. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | conferencePaper |
| fulltext.url.fl_str_mv | https://bibliotecadigital.ipb.pt/bitstreams/bc46fa04-f8d4-41b9-bd74-50ccca9cfaa5/download |
| id | ipb_0ef7e2f48caf1fe3d07ebea14728b99a |
| identifier.url.fl_str_mv | http://hdl.handle.net/10198/22709 |
| instacron_str | ipb |
| institution | Instituto Politécnico de Bragança |
| instname_str | Instituto Politécnico de Bragança |
| language | eng |
| network_acronym_str | ipb |
| network_name_str | Biblioteca Digital do IPB |
| oai_identifier_str | oai:bibliotecadigital.ipb.pt:10198/22709 |
| organization_str_mv | urn:organizationAcronym:ipb |
| person_str_mv | Martins, Maria Prudência Martins, Maria Prudência https://www.ciencia-id.pt/4C16-9EE4-B35D 4C16-9EE4-B35D http://orcid.org/0000-0001-9281-7138 0000-0001-9281-7138 Miguéis, Vera Fonseca, Davide Alves, Albano Alves, Albano https://www.ciencia-id.pt/281A-DD4A-2605 281A-DD4A-2605 http://orcid.org/0000-0001-9796-6810 0000-0001-9796-6810 |
| publishDate | 2019 |
| publisher.none.fl_str_mv | Springer Nature Switzerland AG 2019 |
| reponame_str | Biblioteca Digital do IPB |
| repository_id_str | urn:repositoryAcronym:ipb |
| service_str_mv | urn:repositoryAcronym:ipb |
| spelling | engSpringer Nature Switzerland AG 2019pt_PTThe 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.application/pdfpt_PTA data mining approach for predicting academic success – a case studyPersonalMartins, Maria PrudênciaDSpacehttp://dspace.org/items/43e52986-3314-423b-a1b3-f634412c58a1DSpacehttp://dspace.org/items/43e52986-3314-423b-a1b3-f634412c58a1MartinsMaria PrudênciaCiência IDhttps://www.ciencia-id.pt4C16-9EE4-B35DORCIDhttp://orcid.org0000-0001-9281-7138Miguéis, VeraFonseca, DavidePersonalAlves, AlbanoDSpacehttp://dspace.org/items/80d7f985-d700-4911-8974-b2678816db35DSpacehttp://dspace.org/items/80d7f985-d700-4911-8974-b2678816db35AlvesAlbanoCiência IDhttps://www.ciencia-id.pt281A-DD4A-2605ORCIDhttp://orcid.org0000-0001-9796-6810HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptDOIIsPartOf10.1007/978-3-030-11890-7_52020-09-09T15:49:27Z20192019-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/22709http://purl.org/coar/access_right/c_abf2open accessData miningEducational data miningAcademic successRandom forestRegression575281 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paper2019http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/bc46fa04-f8d4-41b9-bd74-50ccca9cfaa5/downloadInformation technology and systems: proceeding of ICITS 20199184556Quito - Equador |
| spellingShingle | A data mining approach for predicting academic success – a case study Martins, Maria Prudência Data mining Educational data mining Academic success Random forest Regression |
| status | SINGLETON |
| subject.fl_str_mv | Data mining Educational data mining Academic success Random forest Regression |
| title | A data mining approach for predicting academic success – a case study |
| title_full | A data mining approach for predicting academic success – a case study |
| title_fullStr | A data mining approach for predicting academic success – a case study |
| title_full_unstemmed | A data mining approach for predicting academic success – a case study |
| title_short | A data mining approach for predicting academic success – a case study |
| title_sort | A data mining approach for predicting academic success – a case study |
| topic | Data mining Educational data mining Academic success Random forest Regression |
| topic_facet | Data mining Educational data mining Academic success Random forest Regression |
| url | http://hdl.handle.net/10198/22709 |
| visible | 1 |