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dc.contributor.authorSáiz Manzanares, María Consuelo 
dc.contributor.authorMarticorena Sánchez, Raúl 
dc.contributor.authorSáez García, Javier
dc.contributor.authorGonzález Díez, Irene 
dc.date.accessioned2025-03-03T08:18:59Z
dc.date.available2025-03-03T08:18:59Z
dc.date.issued2024
dc.identifier.urihttp://hdl.handle.net/10259/10271
dc.description.abstractThis study deals with an analysis of the cognitive load indicators produced in virtual simulation tasks through supervised and unsupervised machine learning techniques. The objectives were (1) to identify the most important cognitive load indicators through the use of supervised and unsupervised machine learning techniques; (2) to study which type of task presentation was most effective at reducing the task’s intrinsic load and increasing its germane load; and (3) to propose an explanatory model and find its fit indicators. We worked with a sample of 48 health sciences and biomedical engineering students from the University of Burgos (Spain). The results indicate that being able to see the task before performing it increases the germane load and decreases the intrinsic load. Similarly, allowing students a choice of presentation channel for the task respects how they process information. In addition, indicators of cognitive load were found to be grouped into components of position, speed, psychogalvanic response, and skin conductance. An explanatory model was proposed and obtained acceptable fit indicators.en
dc.description.sponsorshipThe project “Voice assistants and artificial intelligence in Moodle: a path towards a smart university”, SmartLearnUni, Call 2020 R&D&I Projects-RTI Type B, MINISTRY OF SCIENCE AND INNOVATION AND UNIVERSITIES, STATE RESEARCH AGENCY, Government of Spain, grant number PID2020-117111RB-I00”. Specifically, in the part concerning the application of multichannel eye-tracking technology with university students and the project “Specialized and updated training on supporting advance technologies for early childhood education and care professionals and graduates” (eEarlyCare-T), grant number 2021-1-ES01-KA220-SCH-000032661 funded by the EUROPEAN COMMISSION. In particular, the funding has enabled the development of the e-learning classroom and educational materials.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofApplied Sciences. 2024, V. 14, n. 21, 9831es
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectEye trackingen
dc.subjectGalvanic skin responseen
dc.subjectCognitive loaden
dc.subjectSimulation tasksen
dc.subjectMachine learning techniquesen
dc.subject.otherTecnologíaes
dc.subject.otherTechnologyen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.subject.otherPsicologíaes
dc.subject.otherPsychologyen
dc.subject.otherEnseñanza superiores
dc.subject.otherEducation, Higheren
dc.titleAnalysing Virtual Labs Through Integrated Multi-Channel Eye-Tracking Technology: A Proposal for an Explanatory Fit Modelen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.3390/app14219831es
dc.identifier.doi10.3390/app14219831
dc.identifier.essn2076-3417
dc.journal.titleApplied Scienceses
dc.volume.number14es
dc.issue.number21es
dc.page.initial9831es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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