RT info:eu-repo/semantics/article
T1 Analysing Virtual Labs Through Integrated Multi-Channel Eye-Tracking Technology: A Proposal for an Explanatory Fit Model
A1 Sáiz Manzanares, María Consuelo
A1 Marticorena Sánchez, Raúl
A1 Sáez García, Javier
A1 González Díez, Irene
K1 Eye tracking
K1 Galvanic skin response
K1 Cognitive load
K1 Simulation tasks
K1 Machine learning techniques
K1 Tecnología
K1 Technology
K1 Informática
K1 Computer science
K1 Psicología
K1 Psychology
K1 Enseñanza superior
K1 Education, Higher
AB 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.
PB MDPI
YR 2024
FD 2024
LK http://hdl.handle.net/10259/10271
UL http://hdl.handle.net/10259/10271
LA eng
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 15-mar-2025