Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/7755
Título
Using Eye Tracking Technology to Analyse Cognitive Load in Multichannel Activities in University Students
Autor
Publicado en
International Journal of Human–Computer Interaction. 2023
Editorial
Taylor & Francis
Fecha de publicación
2023
ISSN
1044-7318
DOI
10.1080/10447318.2023.2188532
Résumé
Monitoring through the use of eye-tracking technology helps in understanding the cognitive load learners experience when doing tasks. This data gives the teacher and the student important information for improving learning outcomes. This study examined whether students’ participation in a learning virtual laboratory, with a self-regulated video monitored with eye-tracking, would influence their learning outcomes. It also examined whether students’ prior knowledge affected their learning outcomes. Lastly, the study identified clusters related to cognitive load in relevant Areas of Interest vs. non-relevant Areas of Interest. The sample comprised 42 university students of health sciences. The results indicate that participation in the virtual laboratory was related to better learning outcomes. In addition, prior knowledge did not affect cognitive load. A number of different clusters were found related to indicators of cognitive load in relevant and non-relevant AOIs. More applied studies are needed about the effects of monitoring on learning outcomes and on what it means for individualization of learning.
Palabras clave
Self-regulated learning
Cognitive load
Eye tracking
Machine learning
Effective learning
Materia
Enseñanza superior
Education, Higher
Psicología
Psychology
Tecnología
Technology
Versión del editor
Aparece en las colecciones
Documento(s) sujeto(s) a una licencia Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internacional