Publicação
Using academic analytics to predict dropout risk in engineering courses
| Resumo: | The increase of data generated and stored in the educational databases makes it possible to obtain essential information about the teaching and learning process. School dropout and performance problems continue to represent issues which challenge teachers, researchers and higher education institutions to seek solutions. Through the use of academic analytics techniques for data analysis, a sample of 1,844 students between graduates and dropouts on the period between 2007 and 2015 were used as the basis. The methodology followed is essentially quantitative and it allowed to compare student profiles and degrees based on scores, number of attempts and other performance indicators. The data set was processed using Excel software for statistical analysis and R software for data mining using the k-Means and C5.0 algorithms. The propose of a model based on decision trees has as main objectives the generation of standardized instructions, easy interpretation and allow the addition of several possible outcomes, contributing to the decision-making process. The results of this study resulted in contributions which enable higher education institutions to identify students with performance problems and those at risk of dropout and, therefore, allow teachers and course directors to adopt better strategies to increase success and reduce dropout. |
|---|---|
| Autores principais: | Lima, Jhonny de |
| Outros Autores: | Alves, Paulo; Pereira, Maria João; Almeida, Simone |
| Assunto: | Academic analytics Higher education Dropout Education Engineering |
| Ano: | 2018 |
| 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_ | 1867173343500697600 |
|---|---|
| author | Lima, Jhonny de |
| author2 | Alves, Paulo Pereira, Maria João Almeida, Simone |
| author2_role | author author author |
| author_facet | Lima, Jhonny de Alves, Paulo Pereira, Maria João Almeida, Simone |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Lima, Jhonny de\"},{\"Person.name\":\"Alves, Paulo\",\"Person.identifier.orcid\":\"0000-0002-0100-8691\"},{\"Person.name\":\"Pereira, Maria João\",\"Person.identifier.orcid\":\"0000-0001-6323-0071\"},{\"Person.name\":\"Almeida, Simone\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Lima, Jhonny de Alves, Paulo Pereira, Maria João Almeida, Simone |
| datacite.date.Accepted.fl_str_mv | 2018-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2020-03-23T11:58:56Z |
| datacite.date.embargoed.fl_str_mv | 2020-03-23T11:58:56Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Academic analytics Higher education Dropout Education Engineering |
| datacite.titles.title.fl_str_mv | Using academic analytics to predict dropout risk in engineering courses |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Lima, Jhonny de Alves, Paulo Pereira, Maria João Almeida, Simone |
| dc.date.Accepted.fl_str_mv | 2018-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2020-03-23T11:58:56Z |
| dc.date.embargoed.fl_str_mv | 2020-03-23T11:58:56Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/21093 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Academic Conferences and Publishing International |
| 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 | Academic analytics Higher education Dropout Education Engineering |
| dc.title.fl_str_mv | Using academic analytics to predict dropout risk in engineering courses |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | The increase of data generated and stored in the educational databases makes it possible to obtain essential information about the teaching and learning process. School dropout and performance problems continue to represent issues which challenge teachers, researchers and higher education institutions to seek solutions. Through the use of academic analytics techniques for data analysis, a sample of 1,844 students between graduates and dropouts on the period between 2007 and 2015 were used as the basis. The methodology followed is essentially quantitative and it allowed to compare student profiles and degrees based on scores, number of attempts and other performance indicators. The data set was processed using Excel software for statistical analysis and R software for data mining using the k-Means and C5.0 algorithms. The propose of a model based on decision trees has as main objectives the generation of standardized instructions, easy interpretation and allow the addition of several possible outcomes, contributing to the decision-making process. The results of this study resulted in contributions which enable higher education institutions to identify students with performance problems and those at risk of dropout and, therefore, allow teachers and course directors to adopt better strategies to increase success and reduce dropout. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | conferencePaper |
| fulltext.url.fl_str_mv | https://bibliotecadigital.ipb.pt/bitstreams/940b6fd6-b56b-4655-b617-e1a498c511d3/download |
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| identifier.url.fl_str_mv | http://hdl.handle.net/10198/21093 |
| 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:10198/21093 |
| organization_str_mv | urn:organizationAcronym:ipb |
| person_str_mv | Lima, Jhonny de Alves, Paulo Alves, Paulo https://www.ciencia-id.pt/C319-FC42-5B6B C319-FC42-5B6B http://orcid.org/0000-0002-0100-8691 0000-0002-0100-8691 Pereira, Maria João Pereira, Maria João https://www.ciencia-id.pt/C912-4A49-A3B3 C912-4A49-A3B3 http://orcid.org/0000-0001-6323-0071 0000-0001-6323-0071 Almeida, Simone |
| publishDate | 2018 |
| publisher.none.fl_str_mv | Academic Conferences and Publishing International |
| reponame_str | Biblioteca Digital do IPB |
| repository_id_str | urn:repositoryAcronym:ipb |
| service_str_mv | urn:repositoryAcronym:ipb |
| spelling | engAcademic Conferences and Publishing Internationalpt_PTThe increase of data generated and stored in the educational databases makes it possible to obtain essential information about the teaching and learning process. School dropout and performance problems continue to represent issues which challenge teachers, researchers and higher education institutions to seek solutions. Through the use of academic analytics techniques for data analysis, a sample of 1,844 students between graduates and dropouts on the period between 2007 and 2015 were used as the basis. The methodology followed is essentially quantitative and it allowed to compare student profiles and degrees based on scores, number of attempts and other performance indicators. The data set was processed using Excel software for statistical analysis and R software for data mining using the k-Means and C5.0 algorithms. The propose of a model based on decision trees has as main objectives the generation of standardized instructions, easy interpretation and allow the addition of several possible outcomes, contributing to the decision-making process. The results of this study resulted in contributions which enable higher education institutions to identify students with performance problems and those at risk of dropout and, therefore, allow teachers and course directors to adopt better strategies to increase success and reduce dropout.application/pdfpt_PTUsing academic analytics to predict dropout risk in engineering coursesLima, Jhonny dePersonalAlves, PauloDSpacehttp://dspace.org/items/43d3b0cd-8fd9-4194-a9df-9cca66f8726bDSpacehttp://dspace.org/items/43d3b0cd-8fd9-4194-a9df-9cca66f8726bAlvesPauloCiência IDhttps://www.ciencia-id.ptC319-FC42-5B6BORCIDhttp://orcid.org0000-0002-0100-8691Scopus Author IDhttps://www.scopus.com55834442100PersonalPereira, Maria JoãoDSpacehttp://dspace.org/items/a20ccfa6-4e84-4c25-ab0d-8d6ba196ffc2DSpacehttp://dspace.org/items/a20ccfa6-4e84-4c25-ab0d-8d6ba196ffc2PereiraMaria JoãoCiência IDhttps://www.ciencia-id.ptC912-4A49-A3B3ORCIDhttp://orcid.org0000-0001-6323-0071Researcher IDhttps://www.researcherid.comG-5999-2011Scopus Author IDhttps://www.scopus.com13907870300Almeida, SimoneHostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISBNIsPartOf978-1-912764-07-52020-03-23T11:58:56Z20182018-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/21093http://purl.org/coar/access_right/c_abf2open accessAcademic analyticsHigher educationDropoutEducationEngineering4059962 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paper2018http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/940b6fd6-b56b-4655-b617-e1a498c511d3/downloadECEL 2019 - 17th European Conference on E-Learning316321Atenas, Greece |
| spellingShingle | Using academic analytics to predict dropout risk in engineering courses Lima, Jhonny de Academic analytics Higher education Dropout Education Engineering |
| status | SINGLETON |
| subject.fl_str_mv | Academic analytics Higher education Dropout Education Engineering |
| title | Using academic analytics to predict dropout risk in engineering courses |
| title_full | Using academic analytics to predict dropout risk in engineering courses |
| title_fullStr | Using academic analytics to predict dropout risk in engineering courses |
| title_full_unstemmed | Using academic analytics to predict dropout risk in engineering courses |
| title_short | Using academic analytics to predict dropout risk in engineering courses |
| title_sort | Using academic analytics to predict dropout risk in engineering courses |
| topic | Academic analytics Higher education Dropout Education Engineering |
| topic_facet | Academic analytics Higher education Dropout Education Engineering |
| url | http://hdl.handle.net/10198/21093 |
| visible | 1 |