Knowledge extraction from courses and online learning activities

Detalhes bibliográficos
Autor(a) principal: Urbano, Catarina dos Reis
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/150426
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 Knowledge extraction from courses and online learning activitiesLearning AnalyticsEducational Data MiningLearning Management SystemAssociation Rule MiningPartial Least Squares RegressionE-learningMachine LearningDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsTechnological advancement has led to the increasing use of all types of electronic devices, which causes large volumes of data to be constantly generated and stored in repositories. This growth in data through Information Technology (IT) systems makes it necessary to continue its exploration and analysis to support institutions in the decision-making process. Due to the importance of education in society, this field has been the target of several studies over the years. Taking that into account, and knowing that association rules and regression analysis are among the most popular data mining algorithms for finding the hidden patterns in data, the purpose of this paper is to find exciting trends across courses considering the students’ grades, as well as study if, and to what extent, the student’s learning performance is related to their interaction in moodle. The data used were collected through the netp@ and moodle systems, consisting of all student learning data and activities/logs history. This data belongs to students of all masters who attended the academic years between 2012-2013 and 2020- 2021. We chose Sample, Explore, Modify, Model, and Assess (SEMMA) methodology for the applicability of its steps to accomplish the study’s goals. Through the Partial Least Squares Regression (PLSR) algorithm, it was shown that Gestão do Conhecimento, Metodologias de Investigação and Métodos Descritivos de Data Mining are the most importants courses that affect the grades of Dissertation/Work Project/Intership Report in the Business Intelligence specialization. In addition, according to the predictive model, Metodologias de Investigação was the most important variable for predicting the performance of the Dissertation/Work Project/Internship Report of Information Systems and Technologies Management specialization. Finally, the association rules algorithms used were the Apriori, FP-Growth and Eclat. From their results, it was found that courses with continuous assessment methods achieve better academic performance compared to others. Furthermore, higher levels of online interaction are associated with better achievement.Henriques, Roberto André PereiraRUNUrbano, Catarina dos Reis2023-03-13T13:31:22Z2023-01-232023-01-23T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/150426TID:203247132enginfo: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:RCAAP2024-05-22T18:09:52Zoai:run.unl.pt:10362/150426Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:40:11.706044Repositórios 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 Knowledge extraction from courses and online learning activities
title Knowledge extraction from courses and online learning activities
spellingShingle Knowledge extraction from courses and online learning activities
Urbano, Catarina dos Reis
Learning Analytics
Educational Data Mining
Learning Management System
Association Rule Mining
Partial Least Squares Regression
E-learning
Machine Learning
title_short Knowledge extraction from courses and online learning activities
title_full Knowledge extraction from courses and online learning activities
title_fullStr Knowledge extraction from courses and online learning activities
title_full_unstemmed Knowledge extraction from courses and online learning activities
title_sort Knowledge extraction from courses and online learning activities
author Urbano, Catarina dos Reis
author_facet Urbano, Catarina dos Reis
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Urbano, Catarina dos Reis
dc.subject.por.fl_str_mv Learning Analytics
Educational Data Mining
Learning Management System
Association Rule Mining
Partial Least Squares Regression
E-learning
Machine Learning
topic Learning Analytics
Educational Data Mining
Learning Management System
Association Rule Mining
Partial Least Squares Regression
E-learning
Machine Learning
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 2023-03-13T13:31:22Z
2023-01-23
2023-01-23T00:00:00Z
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/10362/150426
TID:203247132
url http://hdl.handle.net/10362/150426
identifier_str_mv TID:203247132
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
_version_ 1833596878650867712

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