Eye Tracking based Learning Style Identification for Learning Management Systems

In recent years, universities have been faced with increasing numbers of students dropping out. This is partly due to the fact that students are limited in their ability to explore individual learning paths through different course materials. However, a promising remedy to this issue is the implementation of adaptive learning management systems. These systems recommend customised learning paths to students - based on their individual learning styles. Learning styles are commonly classified using questionnaires and learning analytics, but both methods are prone to error. Questionnaires may yield superficial responses due to time constraints or lack of motivation, while learning analytics ignore offline learning behaviour. To address these limitations, this study aims to integrating Eye Tracking for a more accurate classification of students' learning styles. Ultimately, this comprehensive approach could not only open up a deeper understanding of subconscious processes, but also provide valuable insights into students' unique learning preferences. Research: As an example of a possible analysis of the eye-tracking stimuli and eye movement recordings available here, as well as the corresponding ILS questionnaire responses, we refer to the following research works, which should also be referred to if necessary: Bittner, D., Nadimpalli, V. K., Grabinger, L., Ezer, T., Hauser, F., & Mottok, J. (2024, June), Uncovering Learning Styles through Eye Tracking and Artificial Intelligence, In 2024 Symposium on Eye Tracking Research and Applications. ETRA. Bittner, D. (2024), Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence. Master’s Thesis, Regensburg University of Applied Sciences (OTH), Regensburg, Germany Bittner, D., Ezer, T., Grabinger, L., Hauser, F., & Mottok, J. (2023). Unveiling the secrets of learning styles: decoding eye movements via machine learning. In ICERI2023 Proceedings (pp. 5153-5162). IATED. Bittner, D., Hauser, F., Nadimpalli, V. K., Grabinger, L., Staufer, S., & Mottok, J. (2023, June). Towards eye tracking based learning style identification. In Proceedings of the 5th European Conference on Software Engineering Education (pp. 138-147). ECSEE. The following descriptions and the previous abstract are part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. and have to be cited accordingly. Experimental Setup: In the followi