Leveraging Eye Tracking in Digital Classrooms: A Step Towards Multimodal Model for Learning Assistance

Byrne, Sean Anthony; Castner, Nora; Kastrati, Ard; Płomecka, Martyna Beata; Schaefer, William P.; Kasneci, Enkelejda; Bylinskii, Zoya

doi:10.1145/3588015.3589197

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Cited by 4 publications

References 20 publications

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From Lenses to Living Rooms: A Policy Brief on Eye Tracking in XR Before the Impending Boom

Byrne,

Castner,

Bozkir

et al. 2024

2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)

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From Lenses to Living Rooms: A Policy Brief on Eye Tracking in XR Before the Impending Boom

Byrne,

Castner,

Bozkir

et al. 2024

2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)

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Intelligent Smart Campus Management System Based on Full-Time Video Surveillance Platform

Zhang

2024

2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

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ClassID: Enabling Student Behavior Attribution from Ambient Classroom Sensing Systems

Patidar,

Ngoon,

Zimmerman

et al. 2024

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Ambient classroom sensing systems offer a scalable and non-intrusive way to find connections between instructor actions and student behaviors, creating data that can improve teaching and learning. While these systems effectively provide aggregate data, getting reliable individual student-level information is difficult due to occlusion or movements. Individual data can help in understanding equitable student participation, but it requires identifiable data or individual instrumentation. We propose ClassID, a data attribution method for within a class session and across multiple sessions of a course without these constraints. For within-session, our approach assigns unique identifiers to 98% of students with 95% accuracy. It significantly reduces multiple ID assignments compared to the baseline approach (3 vs. 167) based on our testing on data from 15 classroom sessions. For across-session attributions, our approach, combined with student attendance, shows higher precision than the state-of-the-art approach (85% vs. 44%) on three courses. Finally, we present a set of four use cases to demonstrate how individual behavior attribution can enable a rich set of learning analytics, which is not possible with aggregate data alone.

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Leveraging Eye Tracking in Digital Classrooms: A Step Towards Multimodal Model for Learning Assistance

Cited by 4 publications

References 20 publications

From Lenses to Living Rooms: A Policy Brief on Eye Tracking in XR Before the Impending Boom

From Lenses to Living Rooms: A Policy Brief on Eye Tracking in XR Before the Impending Boom

Intelligent Smart Campus Management System Based on Full-Time Video Surveillance Platform

ClassID: Enabling Student Behavior Attribution from Ambient Classroom Sensing Systems

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