2026-06-10T15:37:22Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/102712025-03-04T11:34:53Zcom_10259_5841com_10259_5086com_10259_2604col_10259_5842

Sáiz Manzanares, María Consuelo Marticorena Sánchez, Raúl Sáez García, Javier González Díez, Irene 2025-03-03T08:18:59Z 2025-03-03T08:18:59Z 2024 http://hdl.handle.net/10259/10271 10.3390/app14219831 2076-3417 This 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. The 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. application/pdf eng MDPI Applied Sciences. 2024, V. 14, n. 21, 9831 https://doi.org/10.3390/app14219831 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Eye tracking Galvanic skin response Cognitive load Simulation tasks Machine learning techniques Tecnología Informática Psicología Enseñanza superior Technology Computer science Psychology Education, Higher Analysing Virtual Labs Through Integrated Multi-Channel Eye-Tracking Technology: A Proposal for an Explanatory Fit Model info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Applied Sciences 14 21 9831