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

Bibliographic Details
Main Author: Costa-Mendes, Ricardo
Publication Date: 2020
Other Authors: Oliveira, Tiago, Castelli, Mauro, Cruz-Jesus, Frederico
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/104072
Summary: 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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network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:47:31Zoai:run.unl.pt:10362/104072Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:18:42.273104Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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