Utilizing Convolutional Neural Networks and Eye-Tracking Data for Classroom Attention Tracking

Simple item page
dc.contributor.authorBoswell, Bradley
dc.contributor.authorSanders, Andrew
dc.contributor.authorWalia, Gursimran
dc.contributor.authorAllen, Andrew
dc.date.accessioned2023-12-26T18:46:23Z
dc.date.available2023-12-26T18:46:23Z
dc.date.issued2024-01-03
dc.identifier.doihttps://doi.org/10.24251/HICSS.2024.636
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other2ee27558-4d80-4029-97b7-cd5a54975b45
dc.identifier.urihttps://hdl.handle.net/10125/107021
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectEdTech and Emerging Technologies
dc.subjectattention
dc.subjectcomputer vision
dc.subjecteducation technology
dc.subjecteye-tracking
dc.subjectmachine learning
dc.titleUtilizing Convolutional Neural Networks and Eye-Tracking Data for Classroom Attention Tracking
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractInstructors often use facial cues of their students as key indicators of student attention levels. However, this method can pose a problem in online and computer-based learning environments. While other research has shown computer vision and eye-tracking could be used with machine learning techniques to predict attentiveness, they have shown only moderate success in terms of accuracy. In this work, we improve upon existing techniques for student attention tracking. We employed our previously developed Non-Intrusive Classroom Attention Tracking System (NiCATS) to collect facial images and eye-tracking data of students during three controlled experiments that represent common academic scenarios. Our first contribution is using convolutional neural networks to predict student attentiveness with an F1-Score of 0.91. Our second contribution is the validation of using eye-tracking metrics in conjunction with machine learning models to predict the attentiveness of students with up to 0.78 F1-Score, which could be useful when webcam privacy is a concern.
dcterms.extent9 pages
prism.startingpage5298

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
0520.pdf
Size:
899.23 KB
Format:
Adobe Portable Document Format
Download

Collections

EdTech and Emerging Technologies