Improving the matching of candidates to master programs through educational data mining techniques
Autor(a) principal: | |
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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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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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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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 |
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1799138136051154944 |