Publicação
Modelling academic dropout in computer engineering using arti cial neural networks
| Resumo: | School dropout in higher education is an academic, economic, political and social problem, which has a great impact and is difficult to resolve. In order to mitigate this problem, this paper proposes a predictive model of classification, based on artificial neural networks, which allows the prediction, at the end of the first school year, of the propensity that the computer engineering students of a polytechnic institute in the interior of the country have for dropout. A differentiating aspect of this study is that it considers the classifications obtained in the course units of the first academic year as potential predictors of dropout. A new approach in the process of selecting the factors that foreshadow the dropout allowed isolating 12 explanatory variables, which guaranteed a good predictive capacity of the model (AUC = 78.5%). These variables reveal fundamental aspects for the adoption of management strategies that may be more assertive in the combat to academic dropout. |
|---|---|
| Autores principais: | Camelo, Diogo |
| Outros Autores: | Santos, João C.C.; Martins, Maria Prudência; Gouveia, Paulo D.F. |
| Assunto: | Educational data mining Artificial neural network Academic dropout Predictive model |
| Ano: | 2021 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| _version_ | 1867173244858007552 |
|---|---|
| author | Camelo, Diogo |
| author2 | Santos, João C.C. Martins, Maria Prudência Gouveia, Paulo D.F. |
| author2_role | author author author |
| author_facet | Camelo, Diogo Santos, João C.C. Martins, Maria Prudência Gouveia, Paulo D.F. |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Camelo, Diogo\"},{\"Person.name\":\"Santos, João C.C.\"},{\"Person.name\":\"Martins, Maria Prudência\",\"Person.identifier.orcid\":\"0000-0001-9281-7138\"},{\"Person.name\":\"Gouveia, Paulo D.F.\",\"Person.identifier.orcid\":\"0000-0003-3049-6230\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Camelo, Diogo Santos, João C.C. Martins, Maria Prudência Gouveia, Paulo D.F. |
| datacite.date.Accepted.fl_str_mv | 2021-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2023-02-17T09:52:37Z |
| datacite.date.embargoed.fl_str_mv | 2023-02-17T09:52:37Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| datacite.subjects.subject.fl_str_mv | Educational data mining Artificial neural network Academic dropout Predictive model |
| datacite.titles.title.fl_str_mv | Modelling academic dropout in computer engineering using arti cial neural networks |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Camelo, Diogo Santos, João C.C. Martins, Maria Prudência Gouveia, Paulo D.F. |
| dc.date.Accepted.fl_str_mv | 2021-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2023-02-17T09:52:37Z |
| dc.date.embargoed.fl_str_mv | 2023-02-17T09:52:37Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/27023 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Springer International Publishing |
| 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_16ec |
| dc.subject.none.fl_str_mv | Educational data mining Artificial neural network Academic dropout Predictive model |
| dc.title.fl_str_mv | Modelling academic dropout in computer engineering using arti cial neural networks |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | School dropout in higher education is an academic, economic, political and social problem, which has a great impact and is difficult to resolve. In order to mitigate this problem, this paper proposes a predictive model of classification, based on artificial neural networks, which allows the prediction, at the end of the first school year, of the propensity that the computer engineering students of a polytechnic institute in the interior of the country have for dropout. A differentiating aspect of this study is that it considers the classifications obtained in the course units of the first academic year as potential predictors of dropout. A new approach in the process of selecting the factors that foreshadow the dropout allowed isolating 12 explanatory variables, which guaranteed a good predictive capacity of the model (AUC = 78.5%). These variables reveal fundamental aspects for the adoption of management strategies that may be more assertive in the combat to academic dropout. |
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| format | conferencePaper |
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| funding.funder.alternateName_str_mv | FCT FCT |
| funding.funder.identifier_str_mv | http://doi.org/10.13039/501100001871 http://doi.org/10.13039/501100001871 |
| funding.funder.name_str_mv | Fundação para a Ciência e a Tecnologia Fundação para a Ciência e a Tecnologia |
| funding.name_str_mv | 6817 - DCRRNI ID 6817 - DCRRNI ID |
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| identifier.url.fl_str_mv | http://hdl.handle.net/10198/27023 |
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| institution | Instituto Politécnico de Bragança |
| instname_str | Instituto Politécnico de Bragança |
| language | eng |
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| oai_identifier_str | oai:bibliotecadigital.ipb.pt:10198/27023 |
| organization_str_mv | urn:organizationAcronym:ipb |
| person_str_mv | Camelo, Diogo Santos, João C.C. 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 Gouveia, Paulo D.F. Gouveia, Paulo D.F. http://orcid.org/0000-0003-3049-6230 0000-0003-3049-6230 |
| publishDate | 2021 |
| publisher.none.fl_str_mv | Springer International Publishing |
| reponame_str | Biblioteca Digital do IPB |
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| spelling | engSpringer International Publishingpt_PTSchool dropout in higher education is an academic, economic, political and social problem, which has a great impact and is difficult to resolve. In order to mitigate this problem, this paper proposes a predictive model of classification, based on artificial neural networks, which allows the prediction, at the end of the first school year, of the propensity that the computer engineering students of a polytechnic institute in the interior of the country have for dropout. A differentiating aspect of this study is that it considers the classifications obtained in the course units of the first academic year as potential predictors of dropout. A new approach in the process of selecting the factors that foreshadow the dropout allowed isolating 12 explanatory variables, which guaranteed a good predictive capacity of the model (AUC = 78.5%). These variables reveal fundamental aspects for the adoption of management strategies that may be more assertive in the combat to academic dropout.application/pdfpt_PTModelling academic dropout in computer engineering using arti cial neural networksCamelo, DiogoSantos, João C.C.PersonalMartins, 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.org0000-0001-9281-7138PersonalGouveia, Paulo D.F.DSpacehttp://dspace.org/items/41c37437-90c4-4e40-893b-44fe4ae1f159DSpacehttp://dspace.org/items/41c37437-90c4-4e40-893b-44fe4ae1f159GouveiaPaulo D.F.ORCIDhttp://orcid.org0000-0003-3049-6230Scopus Author IDhttps://www.scopus.com20433578000HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISBNIsPartOf978-3-030-72650-8DOIIsPartOf10.1007/978-3-030-72651-5_142023-02-17T09:52:37Z20212021-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/27023http://purl.org/coar/access_right/c_16ecrestricted accessEducational data miningArtificial neural networkAcademic dropoutPredictive model421460 bytesFundação para a Ciência e a TecnologiaElectromechatronic Systems Research Centre6817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871Fundação para a Ciência e a TecnologiaElectromechatronic Systems Research Centre6817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871other research producthttp://purl.org/coar/resource_type/c_5794conference paper2021http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/c347e9f3-ea23-4123-8f30-cb9405332a82/downloadTrends and applications in information systems and technologies1366141150 |
| spellingShingle | Modelling academic dropout in computer engineering using arti cial neural networks Camelo, Diogo Educational data mining Artificial neural network Academic dropout Predictive model |
| status | SINGLETON |
| subject.fl_str_mv | Educational data mining Artificial neural network Academic dropout Predictive model |
| title | Modelling academic dropout in computer engineering using arti cial neural networks |
| title_full | Modelling academic dropout in computer engineering using arti cial neural networks |
| title_fullStr | Modelling academic dropout in computer engineering using arti cial neural networks |
| title_full_unstemmed | Modelling academic dropout in computer engineering using arti cial neural networks |
| title_short | Modelling academic dropout in computer engineering using arti cial neural networks |
| title_sort | Modelling academic dropout in computer engineering using arti cial neural networks |
| topic | Educational data mining Artificial neural network Academic dropout Predictive model |
| topic_facet | Educational data mining Artificial neural network Academic dropout Predictive model |
| url | http://hdl.handle.net/10198/27023 |
| visible | 1 |