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Machine Learning applied to student attentiveness detection

2023-12-01Artigo científico Acesso aberto
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dc.contributor.authorElbawab, Mohamed
dc.contributor.authorHenriques, Roberto
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblSpringer
dc.date.accessioned2023-05-08T22:11:19Z
dc.date.available2023-05-08T22:11:19Z
dc.date.issued2023-12-01
dc.descriptionElbawab, 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.
dc.description.abstractElectronic 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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent21
dc.format.extent1355659
dc.identifier.doi10.1007/s10639-023-11814-5
dc.identifier.issn1360-2357
dc.identifier.otherPURE: 59965790
dc.identifier.otherPURE UUID: 728d2f45-1380-44b4-96c3-d6280fd39f5b
dc.identifier.otherScopus: 85154553345
dc.identifier.otherWOS: 000984072000002
dc.identifier.otherORCID: /0000-0002-4862-8177/work/152174882
dc.identifier.urihttp://hdl.handle.net/10362/152535
dc.identifier.urlhttps://www.scopus.com/pages/publications/85154553345
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:000984072000002
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.subjectMachine Learning
dc.subjectE-learning
dc.subjectLearning Analytics
dc.subjectExtreme gradient boosting
dc.subjectAccuracy
dc.subjectAUROC
dc.subjectEducation
dc.subjectLibrary and Information Sciences
dc.subjectSDG 4 - Quality Education
dc.titleMachine Learning applied to student attentiveness detectionen
dc.title.subtitleUsing emotional and non-emotional measuresen
dc.typejournal article
degois.publication.firstPage
degois.publication.issue12
degois.publication.lastPage
degois.publication.titleEducation and Information Technologies
degois.publication.volume28
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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