The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Eye tracking in Educational Science: Theoretical frameworks and research agendas
Abstract
Eye tracking is increasingly being used in Educational Science and so has the interest of the eye tracking community grown in this topic. In this paper we briefly introduce the discipline of Educational Science and why it might be interesting to couple it with eye tracking research. We then introduce three major research areas in Educational Science that have already successfully used eye tracking: First, eye tracking has been used to improve the instructional design of computer-based learning and testing environments, often using hyper- or multimedia. Second, eye tracking has shed light on expertise and its development in visual domains, such as chess or medicine. Third, eye tracking has recently been also used to promote visual expertise by means of eye movement modeling examples. We outline the main educational theories for these research areas and indicate where further eye tracking research is needed to expand them.
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“Eye-tracking is widely used also in the education field, where several studies evidenced that there are significant differences in eye movements between experienced and less experienced participants [10,39,40].”
From Impact of Think-Aloud on Eye-Tracking: A Comparison of Concurrent and Retrospective Think-Aloud for Research on Decision-Making in the Game Environment (2020) · cites this paper as [39]
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“Several studies [10,39,40] claim a significant difference in data gathered by eye-tracking when a monitored task is performed by participants with different levels of skills, experience, or education.”
From Impact of Think-Aloud on Eye-Tracking: A Comparison of Concurrent and Retrospective Think-Aloud for Research on Decision-Making in the Game Environment (2020) · cites this paper as [39]
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“Indeed, decades of research have shown that studying examples of a model successfully executing a task is more efficient for learning than learning by trial-and-error (Kirschner, Sweller, & Clark, 2006).”
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Cited by
Multimodal data to design visual learning analytics for understanding regulation of learning
Noroozi, O., Alikhani, I., Järvelä, S., Kirschner, P. A., Juuso, I., & Seppänen, T. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.12.019 · Cited by 140
Digital transformations of classrooms in virtual reality
Gao, H., Bozkir, E., Hasenbein, L., Hahn, J.-U., Göllner, R., & Kasneci, E. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445596 · Cited by 77
The fundamentals of eye tracking part 1: the link between theory and research question
Hessels, R. S., Nuthmann, A., Nyström, M., Andersson, R., Niehorster, D. C., & Hooge, I. T. C. (2024) · Behavior Research Methods · doi:10.3758/s13428-024-02544-8 · Cited by 70
Prokop, M., Pilař, L., & Tichá, I. (2020) · Sensors · doi:10.3390/s20102750 · Cited by 48
Bouwer, R., & Dirkx, K. (2023) · Learning and Instruction · doi:10.1016/j.learninstruc.2023.101745 · Cited by 37
Metadata
- Authors
- Jarodzka, H., Holmqvist, K., & Gruber, H.
- Venue
- Journal of Eye Movement Research
- Publisher
- MDPI AG
- Published
- 2017-02-04
- Volume
- 10
- Issue
- 1
- ISSN
- 1995-8692
- Cited by
- 142
- References
- 13
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
