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Supervising and Improving Attentiveness in Human Computer Interaction

dc.contributor.authorDurães, Dalila
dc.contributor.authorCarneiro, Davide
dc.contributor.authorBajo, Javier
dc.contributor.authorNovais, Paulo
dc.date.accessioned2017-07-19T15:15:21Z
dc.date.available2017-07-19T15:15:21Z
dc.date.issued2016
dc.description.abstractThe collection, storage, management, and anticipation of contextual information about the user to support decision-making constitute some of the key operations in most Ambient Intelligent (AmI) systems. When the instructor has a computer-based class it is often difficult to confirm if the students are working in the proposed activities. In order to mitigate problems that might occur in an environment with learning technologies we suggest an AmI system aimed at capturing, measuring, and supervising the students’ level of attentiveness in real scenarios and dynamically provide recommendations to the instructor. With this system it is possible to assess both individual and group attention, in real-time, providing a measure of the level of engagement of each student in the proposed activities and allowing the instructor to better steer teaching methodologies.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3233/978-1-61499-690-3-255pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/10106
dc.language.isoengpt_PT
dc.publisherIOS Presspt_PT
dc.subjectAmbient Intelligencept_PT
dc.subjectLearning Stylespt_PT
dc.subjectInnovative Scenariospt_PT
dc.subjectAttentivenesspt_PT
dc.titleSupervising and Improving Attentiveness in Human Computer Interactionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleIntelligent Environmentspt_PT
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT

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