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dc.contributor.authorPrieto, Luis P.
dc.contributor.authorSharma, Kshitij
dc.contributor.authorKidzinski, Lukasz
dc.contributor.authorDillenbourg, Pierre
dc.date.accessioned2026-02-26T22:32:30Z
dc.date.available2026-02-26T22:32:30Z
dc.date.issued2018
dc.identifier.citationIEEE Transactions on Learning Technologies, vol. 11, no. 2, pp. 216-229es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/83189
dc.descriptionProducción Científicaes
dc.description.abstractOrchestration load is the effort a teacher spends in coordinating multiple activities and learning processes. It has been proposed as a construct to evaluate the usability of learning technologies at the classroom level, in the same way that cognitive load is used as a measure of usability at the individual level. However, so far this notion has remained abstract. In order to ground orchestration load in empirical evidence and study it in a more systematic and detailed manner, we propose a method to quantify it, based on physiological data (concretely, mobile eye-tracking measures), along with human-coded behavioral data. This paper presents the results of applying this method to four exploratory case studies, where four teachers orchestrated technology-enhanced face-to-face lessons with primary, secondary school, and university students. The data from these studies provide a first validation of this method in different conditions, and illustrate how it can be used to understand the effect of different classroom factors on orchestration load. From these studies, we also extract empirical insights about classroom orchestration using technology.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherIEEEes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.subject.classificationOrchestrationes
dc.subject.classificationorchestration loades
dc.subject.classificationeye-trackinges
dc.subject.classificationcognitive loades
dc.subject.classificationclassroom studieses
dc.titleOrchestration Load Indicators and Patterns: In-the-Wild Studies Using Mobile Eye-Trackinges
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holderIEEE Xplorees
dc.identifier.doi10.1109/TLT.2017.2690687es
dc.relation.publisherversionhttps://ieeexplore.ieee.org/abstract/document/7891939/es
dc.identifier.publicationfirstpage216es
dc.identifier.publicationissue2es
dc.identifier.publicationlastpage229es
dc.identifier.publicationtitleIEEE Transactions on Learning Technologieses
dc.identifier.publicationvolume11es
dc.peerreviewedSIes
dc.description.projectThis research was supported by a Marie Curie Fellowship within the 7th European Community Framework Programme (MIOCTI, FP7-PEOPLE-2012-IEF project no. 327384).es
dc.identifier.essn1939-1382es
dc.identifier.essn2372-0050es
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones


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