Assessing the impact of academic achievement determinants using Machine Learning methods: evidence from a European country

Detalhes bibliográficos
Autor(a) principal: Afonso, Ana Beatriz Antunes
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/132859
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
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spelling Assessing the impact of academic achievement determinants using Machine Learning methods: evidence from a European countryEducationAcademic AchievementData ScienceDriversSDG 4 - Quality educationDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsAcademic achievement has been of great interest for researchers since the 1950s, although only recently have data science methods started to be applied more systematically. This paper uses the mathematics and Portuguese national exams of the population in Portugal in the 2018/2019 academic year to evaluate the performance between methods and compare what factors affect the results of these exams differently. Furthermore, a new approach is presented to deal with the "black-box" dilemma by creating a set of prototypes with a simple statistical approach applied through Neural Networks, providing an approximation of how much a variable impacts a grade.Jesus, Frederico Miguel Campos Cruz Ribeiro deRUNAfonso, Ana Beatriz Antunes2022-01-122025-01-12T00:00:00Z2022-01-12T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/132859TID:202944450enginfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:11:32Zoai:run.unl.pt:10362/132859Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:47:37.183302Repositório 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 Assessing the impact of academic achievement determinants using Machine Learning methods: evidence from a European country
title Assessing the impact of academic achievement determinants using Machine Learning methods: evidence from a European country
spellingShingle Assessing the impact of academic achievement determinants using Machine Learning methods: evidence from a European country
Afonso, Ana Beatriz Antunes
Education
Academic Achievement
Data Science
Drivers
SDG 4 - Quality education
title_short Assessing the impact of academic achievement determinants using Machine Learning methods: evidence from a European country
title_full Assessing the impact of academic achievement determinants using Machine Learning methods: evidence from a European country
title_fullStr Assessing the impact of academic achievement determinants using Machine Learning methods: evidence from a European country
title_full_unstemmed Assessing the impact of academic achievement determinants using Machine Learning methods: evidence from a European country
title_sort Assessing the impact of academic achievement determinants using Machine Learning methods: evidence from a European country
author Afonso, Ana Beatriz Antunes
author_facet Afonso, Ana Beatriz Antunes
author_role author
dc.contributor.none.fl_str_mv Jesus, Frederico Miguel Campos Cruz Ribeiro de
RUN
dc.contributor.author.fl_str_mv Afonso, Ana Beatriz Antunes
dc.subject.por.fl_str_mv Education
Academic Achievement
Data Science
Drivers
SDG 4 - Quality education
topic Education
Academic Achievement
Data Science
Drivers
SDG 4 - Quality education
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
publishDate 2022
dc.date.none.fl_str_mv 2022-01-12
2022-01-12T00:00:00Z
2025-01-12T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/132859
TID:202944450
url http://hdl.handle.net/10362/132859
identifier_str_mv TID:202944450
dc.language.iso.fl_str_mv eng
language eng
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eu_rights_str_mv embargoedAccess
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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
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repository.mail.fl_str_mv
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