A machine learning approximation of the 2015 Portuguese high school student grades

Detalhes bibliográficos
Autor(a) principal: Costa-Mendes, Ricardo
Data de Publicação: 2020
Outros Autores: Oliveira, Tiago, Castelli, Mauro, Cruz-Jesus, Frederico
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/104072
Resumo: Costa-Mendes, R., Oliveira, T., Castelli, M., & Cruz-Jesus, F. (2021). A machine learning approximation of the 2015 Portuguese high school student grades: A hybrid approach. Education and Information Technologies, 26(2), 1527-1547. https://doi.org/10.1007/s10639-020-10316-y
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spelling A machine learning approximation of the 2015 Portuguese high school student gradesA hybrid approachAcademic achievementHigh school gradesMachine learningRandom forestStackingSupport vector regressionEducationLibrary and Information SciencesSDG 4 - Quality EducationSDG 8 - Decent Work and Economic GrowthCosta-Mendes, R., Oliveira, T., Castelli, M., & Cruz-Jesus, F. (2021). A machine learning approximation of the 2015 Portuguese high school student grades: A hybrid approach. Education and Information Technologies, 26(2), 1527-1547. https://doi.org/10.1007/s10639-020-10316-yThis article uses an anonymous 2014–15 school year dataset from the Directorate-General for Statistics of Education and Science (DGEEC) of the Portuguese Ministry of Education as a means to carry out a predictive power comparison between the classic multilinear regression model and a chosen set of machine learning algorithms. A multilinear regression model is used in parallel with random forest, support vector machine, artificial neural network and extreme gradient boosting machine stacking ensemble implementations. Designing a hybrid analysis is intended where classical statistical analysis and artificial intelligence algorithms are blended to augment the ability to retain valuable conclusions and well-supported results. The machine learning algorithms attain a higher level of predictive ability. In addition, the stacking appropriateness increases as the base learner output correlation matrix determinant increases and the random forest feature importance empirical distributions are correlated with the structure of p-values and the statistical significance test ascertains of the multiple linear model. An information system that supports the nationwide education system should be designed and further structured to collect meaningful and precise data about the full range of academic achievement antecedents. The article concludes that no evidence is found in favour of smaller classes.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNCosta-Mendes, RicardoOliveira, TiagoCastelli, MauroCruz-Jesus, Frederico2020-09-14T22:36:26Z2021-03-012021-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/104072eng1360-2357PURE: 19830262https://doi.org/10.1007/s10639-020-10316-yinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:49:36Zoai:run.unl.pt:10362/104072Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:40:07.245260Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A machine learning approximation of the 2015 Portuguese high school student grades
A hybrid approach
title A machine learning approximation of the 2015 Portuguese high school student grades
spellingShingle A machine learning approximation of the 2015 Portuguese high school student grades
Costa-Mendes, Ricardo
Academic achievement
High school grades
Machine learning
Random forest
Stacking
Support vector regression
Education
Library and Information Sciences
SDG 4 - Quality Education
SDG 8 - Decent Work and Economic Growth
title_short A machine learning approximation of the 2015 Portuguese high school student grades
title_full A machine learning approximation of the 2015 Portuguese high school student grades
title_fullStr A machine learning approximation of the 2015 Portuguese high school student grades
title_full_unstemmed A machine learning approximation of the 2015 Portuguese high school student grades
title_sort A machine learning approximation of the 2015 Portuguese high school student grades
author Costa-Mendes, Ricardo
author_facet Costa-Mendes, Ricardo
Oliveira, Tiago
Castelli, Mauro
Cruz-Jesus, Frederico
author_role author
author2 Oliveira, Tiago
Castelli, Mauro
Cruz-Jesus, Frederico
author2_role author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Costa-Mendes, Ricardo
Oliveira, Tiago
Castelli, Mauro
Cruz-Jesus, Frederico
dc.subject.por.fl_str_mv Academic achievement
High school grades
Machine learning
Random forest
Stacking
Support vector regression
Education
Library and Information Sciences
SDG 4 - Quality Education
SDG 8 - Decent Work and Economic Growth
topic Academic achievement
High school grades
Machine learning
Random forest
Stacking
Support vector regression
Education
Library and Information Sciences
SDG 4 - Quality Education
SDG 8 - Decent Work and Economic Growth
description Costa-Mendes, R., Oliveira, T., Castelli, M., & Cruz-Jesus, F. (2021). A machine learning approximation of the 2015 Portuguese high school student grades: A hybrid approach. Education and Information Technologies, 26(2), 1527-1547. https://doi.org/10.1007/s10639-020-10316-y
publishDate 2020
dc.date.none.fl_str_mv 2020-09-14T22:36:26Z
2021-03-01
2021-03-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/104072
url http://hdl.handle.net/10362/104072
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1360-2357
PURE: 19830262
https://doi.org/10.1007/s10639-020-10316-y
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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