Detecting student attentiveness in e-learning using Machine Learning

Detalhes bibliográficos
Autor(a) principal: Elbawab, Mohamed Rabie Khalil Ibrahim
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/10362/148990
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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spelling Detecting student attentiveness in e-learning using Machine LearningMachine LearningE-learningLearning AnalyticsExtreme gradient boostingAccuracyAUROCSDG 4 - Quality educationDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceElectronic learning (e-learning) is considered the new norm of learning. One of the significant drawbacks of e-learning in comparison to the traditional classroom is that teachers cannot monitor the students' attentiveness. Previous literature used physical facial features or emotional states in detecting attentiveness. Other studies proposed combining physical and emotional facial features; however, a mixed model that only used a webcam was not tested. The study objective is to develop a machine learning (ML) model that automatically estimates students' attentiveness during e-learning classes using only a webcam. The model would help in evaluating teaching methods for e-learning. This study collected videos from seven students. The webcam of personal computers is used to obtain a video, from which we build a feature set that characterizes a student's physical and emotional state based on their face. This characterization includes eye aspect ratio (EAR), Yawn aspect ratio (YAR), head pose and emotional states. A total of eleven variables are used in the training and validation of the model. ML algorithms are used to estimate individual students' attention levels. The ML models tested are decision trees, random forests, support vector machines (SVM) and extreme gradient boosting (XGBoost). Human observers' estimation of attention level is used as a reference. Our best attention classifier is the XGBoost, which achieved an average accuracy of 80.52%, with an AUROC OVR of 92.12%. The results indicate that a combination of emotional and non-emotional measures can generate a classifier with an accuracy comparable to other attentiveness studies. The study would also help assess the e-learning lectures through students' attentiveness. Hence will assist in developing the e-learning lectures by generating an attentiveness report for the tested lecture.Henriques, Roberto André PereiraRUNElbawab, Mohamed Rabie Khalil Ibrahim2023-02-10T12:28:01Z2023-01-242023-01-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/148990TID:203220307enginfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-10T16:12:09ZPortal AgregadorONG
dc.title.none.fl_str_mv Detecting student attentiveness in e-learning using Machine Learning
title Detecting student attentiveness in e-learning using Machine Learning
spellingShingle Detecting student attentiveness in e-learning using Machine Learning
Elbawab, Mohamed Rabie Khalil Ibrahim
Machine Learning
E-learning
Learning Analytics
Extreme gradient boosting
Accuracy
AUROC
SDG 4 - Quality education
title_short Detecting student attentiveness in e-learning using Machine Learning
title_full Detecting student attentiveness in e-learning using Machine Learning
title_fullStr Detecting student attentiveness in e-learning using Machine Learning
title_full_unstemmed Detecting student attentiveness in e-learning using Machine Learning
title_sort Detecting student attentiveness in e-learning using Machine Learning
author Elbawab, Mohamed Rabie Khalil Ibrahim
author_facet Elbawab, Mohamed Rabie Khalil Ibrahim
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Elbawab, Mohamed Rabie Khalil Ibrahim
dc.subject.por.fl_str_mv Machine Learning
E-learning
Learning Analytics
Extreme gradient boosting
Accuracy
AUROC
SDG 4 - Quality education
topic Machine Learning
E-learning
Learning Analytics
Extreme gradient boosting
Accuracy
AUROC
SDG 4 - Quality education
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2023
dc.date.none.fl_str_mv 2023-02-10T12:28:01Z
2023-01-24
2023-01-24T00:00:00Z
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/10362/148990
TID:203220307
url http://hdl.handle.net/10362/148990
identifier_str_mv TID:203220307
dc.language.iso.fl_str_mv eng
language eng
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