The main features that influence the academic success of bachelors’ students at Nova School of Business and Economics

Detalhes bibliográficos
Autor(a) principal: Jacob, David Lázaro
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/10362/132927
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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spelling The main features that influence the academic success of bachelors’ students at Nova School of Business and EconomicsAcademic SuccessStudent SuccessEducational Data MiningMachine LearningFeaturesSDG 4 - Quality educationDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe prediction of academic success is a major topic in higher education, especially among the academic community. In this dissertation, we are going to present a data mining approach taking into consideration the features that are the most relevant in terms of successful academic achievement of the Bachelors’ programs at Nova School of Business and Economics (Nova SBE). Initially, we are going to perform a literature review in order to understand the framework of academic success and also to make a summary of previous research on the field of educational data mining when used to assess student success. Subsequently, the empirical approach will start being developed with the extraction of socio-economic, socio-demographic, and academic data of students, which will result in our main dataset. Later, and after the data discovery, data cleansing, and transformation activities, a set of features are going to be taken into consideration according to their relevance for the subject. Based on the dataset containing these features, several predictive data-driven techniques are going to be applied, resulting in models which are going to be assessed in order to understand if the selected features are relevant enough to answer our problem or if there is a need to substitute them by other attributes. This process will result in several iterations that will confer credibility and robustness to the model that demonstrates the best performance in classifying students’ academic success. In the end, it is intended that the insights extracted from the model will provide the school key stakeholders with enough knowledge to capacitate them to take actions that will result in the maximization of the students learning success.Henriques, Roberto André PereiraRUNJacob, David Lázaro2022-02-15T15:56:12Z2022-01-182022-01-18T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/132927TID:202941493enginfo: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:RCAAP2024-03-11T05:11:36Zoai:run.unl.pt:10362/132927Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:47:38.566327Repositório 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 The main features that influence the academic success of bachelors’ students at Nova School of Business and Economics
title The main features that influence the academic success of bachelors’ students at Nova School of Business and Economics
spellingShingle The main features that influence the academic success of bachelors’ students at Nova School of Business and Economics
Jacob, David Lázaro
Academic Success
Student Success
Educational Data Mining
Machine Learning
Features
SDG 4 - Quality education
title_short The main features that influence the academic success of bachelors’ students at Nova School of Business and Economics
title_full The main features that influence the academic success of bachelors’ students at Nova School of Business and Economics
title_fullStr The main features that influence the academic success of bachelors’ students at Nova School of Business and Economics
title_full_unstemmed The main features that influence the academic success of bachelors’ students at Nova School of Business and Economics
title_sort The main features that influence the academic success of bachelors’ students at Nova School of Business and Economics
author Jacob, David Lázaro
author_facet Jacob, David Lázaro
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Jacob, David Lázaro
dc.subject.por.fl_str_mv Academic Success
Student Success
Educational Data Mining
Machine Learning
Features
SDG 4 - Quality education
topic Academic Success
Student Success
Educational Data Mining
Machine Learning
Features
SDG 4 - Quality education
description Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2022
dc.date.none.fl_str_mv 2022-02-15T15:56:12Z
2022-01-18
2022-01-18T00:00:00Z
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/10362/132927
TID:202941493
url http://hdl.handle.net/10362/132927
identifier_str_mv TID:202941493
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
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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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