Using Machine Learning Models to predict high school student’s Academic Achievement

Bibliographic Details
Main Author: Quintino, Afonso João Mendes
Publication Date: 2024
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/174256
Summary: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
id RCAP_ebe9eb927459489ab48e918f6edde3da
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network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling Using Machine Learning Models to predict high school student’s Academic AchievementEducationAcademic AchievementArtificial IntelligenceMachine LearningSDG 4 - Quality educationSDG 5 - Gender equalitySDG 10 - Reduced inequalitiesDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsUnderstanding student dropout has become increasingly relevant given the growing importance of educated people in today’s workforce. Therefore, predicting a student’s academic achievement (AA), whether he/she passes the academic year or not, can prove crucial to assisting teachers and competent decision-makersto create measures to help retain and eventually reduce academic abandonment. To address such issues, this paper utilizes Machine Learning (ML) models to obtain accurate predictions of essentially every student’s AA in Portuguese public high schools using data from the Portuguese Ministry of Education and understand what are the drivers of AA that most affect the predictive abilities of said models. Our results show that Random Forest and XGBoost have similar levels of accuracy, however, the latter displayed slightly better predictions. Regarding the most influential AA drivers, previous retention, gender, and the location of the student’s school were the ones that showed the greatest effect on the XGBoost model’s ability to accurately predict the student’s success. Several suggestions are made to educational stakeholders on the results of this study.Jesus, Frederico Miguel Campos Cruz Ribeiro deRUNQuintino, Afonso João Mendes2024-10-29T14:30:42Z2024-10-232024-10-23T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/174256TID:203777034enginfo: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:RCAAP2025-01-13T01:40:35Zoai:run.unl.pt:10362/174256Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:12:39.281721Repositórios 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 Using Machine Learning Models to predict high school student’s Academic Achievement
title Using Machine Learning Models to predict high school student’s Academic Achievement
spellingShingle Using Machine Learning Models to predict high school student’s Academic Achievement
Quintino, Afonso João Mendes
Education
Academic Achievement
Artificial Intelligence
Machine Learning
SDG 4 - Quality education
SDG 5 - Gender equality
SDG 10 - Reduced inequalities
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Using Machine Learning Models to predict high school student’s Academic Achievement
title_full Using Machine Learning Models to predict high school student’s Academic Achievement
title_fullStr Using Machine Learning Models to predict high school student’s Academic Achievement
title_full_unstemmed Using Machine Learning Models to predict high school student’s Academic Achievement
title_sort Using Machine Learning Models to predict high school student’s Academic Achievement
author Quintino, Afonso João Mendes
author_facet Quintino, Afonso João Mendes
author_role author
dc.contributor.none.fl_str_mv Jesus, Frederico Miguel Campos Cruz Ribeiro de
RUN
dc.contributor.author.fl_str_mv Quintino, Afonso João Mendes
dc.subject.por.fl_str_mv Education
Academic Achievement
Artificial Intelligence
Machine Learning
SDG 4 - Quality education
SDG 5 - Gender equality
SDG 10 - Reduced inequalities
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Education
Academic Achievement
Artificial Intelligence
Machine Learning
SDG 4 - Quality education
SDG 5 - Gender equality
SDG 10 - Reduced inequalities
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
publishDate 2024
dc.date.none.fl_str_mv 2024-10-29T14:30:42Z
2024-10-23
2024-10-23T00:00:00Z
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/10362/174256
TID:203777034
url http://hdl.handle.net/10362/174256
identifier_str_mv TID:203777034
dc.language.iso.fl_str_mv eng
language eng
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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