Vital Emo: The boredom detector with a machine learning perspective

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dc.conference.title29th International Conference on Computers in Education
dc.contributor.authorEl-Yamri, Meriem
dc.contributor.authorIsar Muñoz, Diego
dc.contributor.authorOrtiz Marchut, Álvaro
dc.contributor.authorPadilla Rodríguez, Daniel
dc.contributor.authorPrieto Ibañez, Sofía
dc.contributor.authorManero Iglesias, José Borja
dc.contributor.authorRos Velasco, Josefa
dc.contributor.editorAsia-Pacific Society for Computers in Education
dc.date.accessioned2024-04-05T16:02:47Z
dc.date.available2024-04-05T16:02:47Z
dc.date.issued2021-04-01
dc.description.abstractBoredom is one of the main problems found in 21st-century educational environments. Today's society is hyperconnected and exposes us to a large number of daily stimuli. This daily overstimulation can negatively affect students who are facing several hours of online classes (especially in the new context derived from COVID-19). In these classes, students experience a shortage of stimuli, resulting in boredom and a shorter attention span. Past studies have shown that certain facial patterns help detect lack of engagement and boredom. The most noticeable are the widening of the eyelids, the opening of the lips, and the gaze direction. As for posture, there are references to using the hand to support the head, maintaining a reclining posture, or hiding part of the face. Its counterpart, interest, can be reflected in patterns such as maintaining a wide-eyed gaze, smiling, or maintaining an upright posture. This paper presents the development, architecture, and evaluation of a tool called VITAL EMO. Through artificial intelligence (tra ined with facial patterns), this tool seeks: 1) to detect, through a camera, students' boredom, and 2) to alert teachers of this situation, allowing them to change the methodology or strategy to reengage the students on the spot. The creation of the dataset used to train the AI was carried out with 12 people. The participants took part telematically; each were recorded as they watched a video and answered questions about it. Despite using a small dataset, the study results show that it is possible to detect facial patterns associated with engagement and boredom, such as smiling, squinting, facial occlusion, or avoiding being directly in front of the camera. These results pave the way for future research with a larger dataset to increase the effectiveness of AI and further develop the VITAL EMO tool.
dc.description.departmentDepto. de Ingeniería de Software e Inteligencia Artificial (ISIA)
dc.description.facultyFac. de Informática
dc.description.refereedTRUE
dc.description.statuspub
dc.identifier.urihttps://hdl.handle.net/20.500.14352/102782
dc.language.isoeng
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096401-A-I00/ES/COMUNICACION EFECTIVA PARA PROFESORADO DE SECUNDARIA MEDIANTE REALIDAD VIRTUAL/
dc.rights.accessRightsopen access
dc.subject.keywordBoredom
dc.subject.keywordEmotions
dc.subject.keywordEducation
dc.subject.keywordStudents
dc.subject.keywordClasses
dc.subject.keywordCamera
dc.subject.keywordVideo
dc.subject.keywordDetector
dc.subject.keywordArtificial intelligence
dc.subject.keywordMachine learning
dc.subject.keywordDeep learning
dc.subject.keywordConvolutional networks
dc.subject.keywordComputer application
dc.subject.keywordVital emo
dc.subject.ucmInformática (Informática)
dc.subject.unesco3304 Tecnología de Los Ordenadores
dc.titleVital Emo: The boredom detector with a machine learning perspective
dc.typeconference paper
dspace.entity.typePublication
relation.isAuthorOfPublication0e709211-9a4b-47f4-90a8-0397add3b32a
relation.isAuthorOfPublicationfae0a960-515b-4682-bba7-7de300d4f55a
relation.isAuthorOfPublication.latestForDiscovery0e709211-9a4b-47f4-90a8-0397add3b32a
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