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

Bibliographic Details
Main Author: Rosário, Nuno Alexandre Lopes do
Publication Date: 2022
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/140856
Summary: Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:02:53Zoai:run.unl.pt:10362/140856Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:33:40.410136Repositórios 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 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: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
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