Multimodal Affective Computing to Enhance the User Experience of Educational Software Applications

Affective computing is becoming more and more important as it enables to extend the possibilities of computing technologies by incorporating emotions. In fact, the detection of users’ emotions has become one of the most important aspects regarding Affective Computing. In this paper, we present an ed...

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Detalles Bibliográficos
Autores: García-García, José María, Penichet, Víctor M. R., Lozano, María Dolores, Garrido, Juan Enrique, Lai-Chong Law, Effie
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/23840
Acceso en línea:https://doi.org/10.1155/2018/8751426
http://hdl.handle.net/10578/23840
Access Level:acceso abierto
Palabra clave:Affective computing
Educational software applications
Users
Emotions detection
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spelling Multimodal Affective Computing to Enhance the User Experience of Educational Software ApplicationsGarcía-García, José MaríaPenichet, Víctor M. R.Lozano, María DoloresGarrido, Juan EnriqueLai-Chong Law, EffieAffective computingEducational software applicationsUsersEmotions detectionAffective computing is becoming more and more important as it enables to extend the possibilities of computing technologies by incorporating emotions. In fact, the detection of users’ emotions has become one of the most important aspects regarding Affective Computing. In this paper, we present an educational software application that incorporates affective computing by detecting the users’ emotional states to adapt its behaviour to the emotions sensed. -is way, we aim at increasing users’ engagement to keep them motivated for longer periods of time, thus improving their learning progress. To prove this, the application has been assessed with real users. -e performance of a set of users using the proposed system has been compared with a control group that used the same system without implementing emotion detection. -e outcomes of this evaluation have shown that our proposed system, incorporating affective computing, produced better results than the one used by the control group.Hindawi202020202018info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.1155/2018/8751426http://hdl.handle.net/10578/23840reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésinfo:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/238402026-05-27T07:36:41Z
dc.title.none.fl_str_mv Multimodal Affective Computing to Enhance the User Experience of Educational Software Applications
title Multimodal Affective Computing to Enhance the User Experience of Educational Software Applications
spellingShingle Multimodal Affective Computing to Enhance the User Experience of Educational Software Applications
García-García, José María
Affective computing
Educational software applications
Users
Emotions detection
title_short Multimodal Affective Computing to Enhance the User Experience of Educational Software Applications
title_full Multimodal Affective Computing to Enhance the User Experience of Educational Software Applications
title_fullStr Multimodal Affective Computing to Enhance the User Experience of Educational Software Applications
title_full_unstemmed Multimodal Affective Computing to Enhance the User Experience of Educational Software Applications
title_sort Multimodal Affective Computing to Enhance the User Experience of Educational Software Applications
dc.creator.none.fl_str_mv García-García, José María
Penichet, Víctor M. R.
Lozano, María Dolores
Garrido, Juan Enrique
Lai-Chong Law, Effie
author García-García, José María
author_facet García-García, José María
Penichet, Víctor M. R.
Lozano, María Dolores
Garrido, Juan Enrique
Lai-Chong Law, Effie
author_role author
author2 Penichet, Víctor M. R.
Lozano, María Dolores
Garrido, Juan Enrique
Lai-Chong Law, Effie
author2_role author
author
author
author
dc.subject.none.fl_str_mv Affective computing
Educational software applications
Users
Emotions detection
topic Affective computing
Educational software applications
Users
Emotions detection
description Affective computing is becoming more and more important as it enables to extend the possibilities of computing technologies by incorporating emotions. In fact, the detection of users’ emotions has become one of the most important aspects regarding Affective Computing. In this paper, we present an educational software application that incorporates affective computing by detecting the users’ emotional states to adapt its behaviour to the emotions sensed. -is way, we aim at increasing users’ engagement to keep them motivated for longer periods of time, thus improving their learning progress. To prove this, the application has been assessed with real users. -e performance of a set of users using the proposed system has been compared with a control group that used the same system without implementing emotion detection. -e outcomes of this evaluation have shown that our proposed system, incorporating affective computing, produced better results than the one used by the control group.
publishDate 2018
dc.date.none.fl_str_mv 2018
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.none.fl_str_mv https://doi.org/10.1155/2018/8751426
http://hdl.handle.net/10578/23840
url https://doi.org/10.1155/2018/8751426
http://hdl.handle.net/10578/23840
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Hindawi
publisher.none.fl_str_mv Hindawi
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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