Machine Learning applied to student attentiveness detection

Detalhes bibliográficos
Autor(a) principal: Elbawab, Mohamed
Data de Publicação: 2023
Outros Autores: Henriques, Roberto
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/152535
Resumo: Elbawab, M., & Henriques, R. (2023). Machine Learning applied to student attentiveness detection: Using emotional and non-emotional measures. Education and Information Technologies, 28(12), 15717–15737. https://doi.org/10.1007/s10639-023-11814-5---Open access funding provided by FCT|FCCN (b-on). This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project—UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Fundação para a Ciência e a Tecnologia,UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS, Roberto Henriques.
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spelling Machine Learning applied to student attentiveness detectionUsing emotional and non-emotional measuresMachine LearningE-learningLearning AnalyticsExtreme gradient boostingAccuracyAUROCEducationLibrary and Information SciencesSDG 4 - Quality EducationElbawab, M., & Henriques, R. (2023). Machine Learning applied to student attentiveness detection: Using emotional and non-emotional measures. Education and Information Technologies, 28(12), 15717–15737. https://doi.org/10.1007/s10639-023-11814-5---Open access funding provided by FCT|FCCN (b-on). This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project—UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Fundação para a Ciência e a Tecnologia,UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS, Roberto Henriques.Electronic 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.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNElbawab, MohamedHenriques, Roberto2023-05-08T22:11:19Z2023-12-012023-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article21application/pdfhttp://hdl.handle.net/10362/152535eng1360-2357PURE: 59965790https://doi.org/10.1007/s10639-023-11814-5info: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:RCAAP2024-03-11T05:34:49Zoai:run.unl.pt:10362/152535Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:55.789943Repositório 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 Machine Learning applied to student attentiveness detection
Using emotional and non-emotional measures
title Machine Learning applied to student attentiveness detection
spellingShingle Machine Learning applied to student attentiveness detection
Elbawab, Mohamed
Machine Learning
E-learning
Learning Analytics
Extreme gradient boosting
Accuracy
AUROC
Education
Library and Information Sciences
SDG 4 - Quality Education
title_short Machine Learning applied to student attentiveness detection
title_full Machine Learning applied to student attentiveness detection
title_fullStr Machine Learning applied to student attentiveness detection
title_full_unstemmed Machine Learning applied to student attentiveness detection
title_sort Machine Learning applied to student attentiveness detection
author Elbawab, Mohamed
author_facet Elbawab, Mohamed
Henriques, Roberto
author_role author
author2 Henriques, Roberto
author2_role author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Elbawab, Mohamed
Henriques, Roberto
dc.subject.por.fl_str_mv Machine Learning
E-learning
Learning Analytics
Extreme gradient boosting
Accuracy
AUROC
Education
Library and Information Sciences
SDG 4 - Quality Education
topic Machine Learning
E-learning
Learning Analytics
Extreme gradient boosting
Accuracy
AUROC
Education
Library and Information Sciences
SDG 4 - Quality Education
description Elbawab, M., & Henriques, R. (2023). Machine Learning applied to student attentiveness detection: Using emotional and non-emotional measures. Education and Information Technologies, 28(12), 15717–15737. https://doi.org/10.1007/s10639-023-11814-5---Open access funding provided by FCT|FCCN (b-on). This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project—UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Fundação para a Ciência e a Tecnologia,UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS, Roberto Henriques.
publishDate 2023
dc.date.none.fl_str_mv 2023-05-08T22:11:19Z
2023-12-01
2023-12-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/152535
url http://hdl.handle.net/10362/152535
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1360-2357
PURE: 59965790
https://doi.org/10.1007/s10639-023-11814-5
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 21
application/pdf
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instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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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
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