Predicting academic performance - A practical study using Moodle log data and sociodemographic traits

Detalhes bibliográficos
Autor(a) principal: Rosário, Nuno Alexandre Lopes do
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/10362/140856
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling Predicting academic performance - A practical study using Moodle log data and sociodemographic traitsPerformance PredictionEducational Data MiningLearning AnalyticsMachine LearningProject Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsWith the increase of computational power, usage of IT systems and tools in several industries also increased the amounts of data generated and stored. Education is one of these fields. The opportunity to utilize analytical and data related techniques to the data generated and stored by computer-based educational systems is more significant than ever. Performance prediction is one of the most popular uses for all the data generated by educational systems. In this line of thought, the main objective of this paper is to build a predictive model capable of classifying a student´s grade based on its Moodle system activity and several sociodemographic variables taken from the Netpa System. All the data used belongs to student´s that attended the first semester of 2019 at Nova Information Management School. To achieve the objective, SEMMA Methodology was implemented. Python Language was used, with particular emphasis on the Scikit-Learn, pandas and Seaborn packages. Raw Moodle logs were processed and transformed into variables that represented the number of times a student navigated to a specific page in the platform. This information was then joined with Netpa variables, and a dataset was built. Exploratory data analysis was performed, and several model configurations were tested. The main differences that separate the models are outlier treatment, sampling techniques, feature scalers, feature engineering and type of algorithm – Logistic Regression, K-Neighbours Classifier, Random Forest Classifier and Multi-Layer Perceptron. Using a K-Neighbours Classifier and the SMOTE sampling technique an F1-Score of 0.624 and a ROC AUC of 0.828 was obtained.Henriques, Roberto André PereiraRUNRosário, Nuno Alexandre Lopes do2022-06-27T14:53:15Z2022-05-122022-05-12T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/140856TID:203028511enginfo: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:RCAAP2024-03-11T05:18:03Zoai:run.unl.pt:10362/140856Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:49:48.636241Repositório 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 Predicting academic performance - A practical study using Moodle log data and sociodemographic traits
title Predicting academic performance - A practical study using Moodle log data and sociodemographic traits
spellingShingle Predicting academic performance - A practical study using Moodle log data and sociodemographic traits
Rosário, Nuno Alexandre Lopes do
Performance Prediction
Educational Data Mining
Learning Analytics
Machine Learning
title_short Predicting academic performance - A practical study using Moodle log data and sociodemographic traits
title_full Predicting academic performance - A practical study using Moodle log data and sociodemographic traits
title_fullStr Predicting academic performance - A practical study using Moodle log data and sociodemographic traits
title_full_unstemmed Predicting academic performance - A practical study using Moodle log data and sociodemographic traits
title_sort Predicting academic performance - A practical study using Moodle log data and sociodemographic traits
author Rosário, Nuno Alexandre Lopes do
author_facet Rosário, Nuno Alexandre Lopes do
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Rosário, Nuno Alexandre Lopes do
dc.subject.por.fl_str_mv Performance Prediction
Educational Data Mining
Learning Analytics
Machine Learning
topic Performance Prediction
Educational Data Mining
Learning Analytics
Machine Learning
description Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2022
dc.date.none.fl_str_mv 2022-06-27T14:53:15Z
2022-05-12
2022-05-12T00:00:00Z
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/10362/140856
TID:203028511
url http://hdl.handle.net/10362/140856
identifier_str_mv TID:203028511
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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