Improving the matching of candidates to master programs through educational data mining techniques

Detalhes bibliográficos
Autor(a) principal: Paulino, Ana Raquel Rego
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/152092
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 Improving the matching of candidates to master programs through educational data mining techniquesLearning AnalyticsEducational Data MiningMachine LearningMaster’s studentsSDG 4 - Quality educationSDG 5 - Gender equalityDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThe matching process of students to their elective master's programs, based on a concrete study case of a faculty, is made from scratch every year and does not consider the history of the candidates through the years. Therefore, and with the rising power of learning analytics and educational data mining, this thesis evaluates the ability employment of automated models, classification, and prediction, to match between students and master program in order to create a new learning tool to enhance learning performance. The families of classifiers under study are ensemble methods of neural networks and or decision trees, Artificial Neural Networks, Support Vector Machines and Instant Based Learning. The results encompass students’ background, personal, academic, and professional data, and seem auspicious to the future pre-selection of suitable candidates since they bring advantages to the system in terms of comprehending better past, current, and future students, above all, the factors that influence the most the fit of students-programs. The best performer algorithm is Random Forests in terms of prediction metrics.Henriques, Roberto André PereiraRUNPaulino, Ana Raquel Rego2023-04-102024-04-10T00:00:00Z2023-04-10T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/152092TID:203268377enginfo: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:RCAAP2024-03-11T05:34:27Zoai:run.unl.pt:10362/152092Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:47.552768Repositório 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 Improving the matching of candidates to master programs through educational data mining techniques
title Improving the matching of candidates to master programs through educational data mining techniques
spellingShingle Improving the matching of candidates to master programs through educational data mining techniques
Paulino, Ana Raquel Rego
Learning Analytics
Educational Data Mining
Machine Learning
Master’s students
SDG 4 - Quality education
SDG 5 - Gender equality
title_short Improving the matching of candidates to master programs through educational data mining techniques
title_full Improving the matching of candidates to master programs through educational data mining techniques
title_fullStr Improving the matching of candidates to master programs through educational data mining techniques
title_full_unstemmed Improving the matching of candidates to master programs through educational data mining techniques
title_sort Improving the matching of candidates to master programs through educational data mining techniques
author Paulino, Ana Raquel Rego
author_facet Paulino, Ana Raquel Rego
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Paulino, Ana Raquel Rego
dc.subject.por.fl_str_mv Learning Analytics
Educational Data Mining
Machine Learning
Master’s students
SDG 4 - Quality education
SDG 5 - Gender equality
topic Learning Analytics
Educational Data Mining
Machine Learning
Master’s students
SDG 4 - Quality education
SDG 5 - Gender equality
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
publishDate 2023
dc.date.none.fl_str_mv 2023-04-10
2023-04-10T00:00:00Z
2024-04-10T00:00:00Z
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/10362/152092
TID:203268377
url http://hdl.handle.net/10362/152092
identifier_str_mv TID:203268377
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: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
repository.mail.fl_str_mv
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