Mathematics and Mother Tongue Academic Achievement

Detalhes bibliográficos
Autor(a) principal: Nunes, Catarina
Data de Publicação: 2022
Outros Autores: Beatriz-Afonso, Ana, Cruz-jesus, Frederico, Oliveira, Tiago, Castelli, Mauro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/143711
Resumo: Nunes, C., Beatriz-Afonso, A., Cruz-jesus, F., Oliveira, T., & Castelli, M. (2022). Mathematics and Mother Tongue Academic Achievement: A Machine Learning Approach. Emerging Science Journal, 6(Special Issue: Current Issues, Trends, and New Ideas in Education), 137-149. https://doi.org/10.28991/ESJ-2022-SIED-010 ----This study was funded by FCT – Fundação para a Ciência e Tecnologia (DSAIPA/DS/0032/2018). Acknowledgements: We also gratefully acknowledge financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal), national funding through research grant Information Management Research Center – MagIC/NOVA IMS (UIDB/04152/2020).
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spelling Mathematics and Mother Tongue Academic AchievementA Machine Learning ApproachAcademic AchievementEducationAcademic AchievemenNetworksMachine LearningGeneralSDG 4 - Quality EducationSDG 8 - Decent Work and Economic GrowthSDG 10 - Reduced InequalitiesNunes, C., Beatriz-Afonso, A., Cruz-jesus, F., Oliveira, T., & Castelli, M. (2022). Mathematics and Mother Tongue Academic Achievement: A Machine Learning Approach. Emerging Science Journal, 6(Special Issue: Current Issues, Trends, and New Ideas in Education), 137-149. https://doi.org/10.28991/ESJ-2022-SIED-010 ----This study was funded by FCT – Fundação para a Ciência e Tecnologia (DSAIPA/DS/0032/2018). Acknowledgements: We also gratefully acknowledge financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal), national funding through research grant Information Management Research Center – MagIC/NOVA IMS (UIDB/04152/2020).Academic achievement is of great interest to education researchers and practitioners. Several academic achievement determinants have been described in the literature, mostly identified by analyzing primary (sample) data with classic statistical methods. Despite their superiority, only recently have machine learning methods started to be applied systematically in this context. However, even when this is the case, the ability to draw conclusions is greatly hampered by the "black-box" effect these methods entail. We contribute to the literature by combining the efficiency of machine learning methods, trained with data from virtually every public upper-secondary student of a European country, with the ability to quantify exactly how much each driver impacts academic achievement on Mathematics and mother tongue, through the use of prototypes. Our results indicate that the most important general academic achievement inhibitor is the previous retainment. Legal guardian's education is a critical driver, especially in Mathematics; whereas gender is especially important for mother tongue, as female students perform better. Implications for research and practice are presented.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNNunes, CatarinaBeatriz-Afonso, AnaCruz-jesus, FredericoOliveira, TiagoCastelli, Mauro2022-09-13T22:47:37Z2022-09-102022-09-10T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article13application/pdfhttp://hdl.handle.net/10362/143711eng2610-9182PURE: 46517955https://doi.org/10.28991/ESJ-2022-SIED-010info: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:RCAAP2024-03-11T05:22:20Zoai:run.unl.pt:10362/143711Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:51:05.877808Repositório 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 Mathematics and Mother Tongue Academic Achievement
A Machine Learning Approach
title Mathematics and Mother Tongue Academic Achievement
spellingShingle Mathematics and Mother Tongue Academic Achievement
Nunes, Catarina
Academic Achievement
Education
Academic Achievemen
Networks
Machine Learning
General
SDG 4 - Quality Education
SDG 8 - Decent Work and Economic Growth
SDG 10 - Reduced Inequalities
title_short Mathematics and Mother Tongue Academic Achievement
title_full Mathematics and Mother Tongue Academic Achievement
title_fullStr Mathematics and Mother Tongue Academic Achievement
title_full_unstemmed Mathematics and Mother Tongue Academic Achievement
title_sort Mathematics and Mother Tongue Academic Achievement
author Nunes, Catarina
author_facet Nunes, Catarina
Beatriz-Afonso, Ana
Cruz-jesus, Frederico
Oliveira, Tiago
Castelli, Mauro
author_role author
author2 Beatriz-Afonso, Ana
Cruz-jesus, Frederico
Oliveira, Tiago
Castelli, Mauro
author2_role author
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 Nunes, Catarina
Beatriz-Afonso, Ana
Cruz-jesus, Frederico
Oliveira, Tiago
Castelli, Mauro
dc.subject.por.fl_str_mv Academic Achievement
Education
Academic Achievemen
Networks
Machine Learning
General
SDG 4 - Quality Education
SDG 8 - Decent Work and Economic Growth
SDG 10 - Reduced Inequalities
topic Academic Achievement
Education
Academic Achievemen
Networks
Machine Learning
General
SDG 4 - Quality Education
SDG 8 - Decent Work and Economic Growth
SDG 10 - Reduced Inequalities
description Nunes, C., Beatriz-Afonso, A., Cruz-jesus, F., Oliveira, T., & Castelli, M. (2022). Mathematics and Mother Tongue Academic Achievement: A Machine Learning Approach. Emerging Science Journal, 6(Special Issue: Current Issues, Trends, and New Ideas in Education), 137-149. https://doi.org/10.28991/ESJ-2022-SIED-010 ----This study was funded by FCT – Fundação para a Ciência e Tecnologia (DSAIPA/DS/0032/2018). Acknowledgements: We also gratefully acknowledge financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal), national funding through research grant Information Management Research Center – MagIC/NOVA IMS (UIDB/04152/2020).
publishDate 2022
dc.date.none.fl_str_mv 2022-09-13T22:47:37Z
2022-09-10
2022-09-10T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/143711
url http://hdl.handle.net/10362/143711
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2610-9182
PURE: 46517955
https://doi.org/10.28991/ESJ-2022-SIED-010
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eu_rights_str_mv openAccess
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