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Towards Eye Tracking based Learning Style Identification

  • The dropout rate at universities has been very high for years. Thereby, the inexperience and lack of knowledge of students in dealing with individual learning paths in various courses of study plays a decisive role. Adaptive learning management systems are suitable countermeasures, in which learners’ learning styles are classified using questionnaires or computationally intensive algorithms before a learning path is suggested accordingly. In this paper, a study design for student learning style classification using eye tracking is presented. Furthermore, qualitative and quantitative analyses clarify certain relationships between students’ eye movements and learning styles. With the help of classification based on eye tracking, the filling out of questionnaires or the integration of computationally or cost-intensive algorithms can be made redundant in the future.

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Metadaten
Author:Dominik BittnerORCiD, Florian HauserORCiD, Vamsi Krishna Nadimpalli, Lisa GrabingerORCiD, Susanne StauferORCiD, Jürgen MottokORCiDGND
DOI:https://doi.org/10.1145/3593663.3593680
ISBN:978-1-4503-9956-2
Parent Title (English):Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023
Publisher:ACM
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2023
Release Date:2023/07/17
First Page:138
Last Page:147
Institutes:Fakultät Elektro- und Informationstechnik
Fakultät Elektro- und Informationstechnik / Laboratory for Safe and Secure Systems (LAS3)
Begutachtungsstatus:peer-reviewed
research focus:Digitalisierung
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG