Using Eye Tracking Technology to Analyse Cognitive Load in Multichannel Activities in University Students

Abstract

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.

Keywords:

• Self-regulated learning
• cognitive load
• eye tracking
• machine learning
• effective learning
• machine learning

Acknowledgements

The cooperation of all participants is appreciated.

Disclosure statement

The authors report there are no competing interests to declare.

Additional information

Funding

This work was supported by the Ministerio de Ciencia e Innovación de España. Proyectos de I + D+i-RTI Tipo B under Grant number [PID2020-117111RB-I00].

Notes on contributors

María Consuelo Sáiz-Manzanares

María Consuelo Sáiz Manzanares holds a PhD in Psychology and Industrial Technology. She is currently a full professor in the Faculty of Health Sciences at the University of Burgos (Spain). Also, she is the director of the research group DATAHES and of the UIC-348. Her research focuses on Metacognitive skills and Artificial Intelligence.

Raúl Marticorena-Sánchez

Raúl Marticorena Sánchez holds a PhD in Computer Engineering and is a senior lecturer at the Polytechnic School of the University of Burgos (Spain). He is a member of the ADMIRABLE research group. His research work focuses on software maintenance and the application of computer engineering and data mining techniques.

Luis J. Martín Antón

Luis J. Martín Antón is a university lecturer in the Faculty of Education and Social Work at the University of Valladolid (UVA) (Spain). He is also a member of the UVA No. 179. His research work focuses on the study of effective learning and academic procrastination behaviour in secondary school and university students.

Irene González-Díez

Irene González Díez a psychologist from the University of Salamanca (USAL), Master in Health Sciences from the University of Burgos (UBU), is currently a pre-doctoral fellow at the GIR DATAHES at UBU doing her doctoral thesis on the application of Advanced Learning Technologies in the study of metacognitive strategies.

Miguel Ángel Carbonero Martín

Miguel Ángel Carbonero Martín holds a PhD in Psychology and Professor in the Faculty of Education and Social Work at the University of Valladolid (UVA) (Spain). He is also the director of the Research Group of the UVA No. 179. His research work focuses on the study of motivational teaching skills of effective teachers.