Deep Learning on Eye Gaze Data to Classify Student Distraction Level in an Educational VR Environment -- Honorable Mention for Best Paper Award

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dc.contributor.authorAsish, Sarker Monojiten_US
dc.contributor.authorHossain, Ekramen_US
dc.contributor.authorKulshreshth, Arun K.en_US
dc.contributor.authorBorst, Christoph W.en_US
dc.contributor.editorOrlosky, Jason and Reiners, Dirk and Weyers, Benjaminen_US
dc.date.accessioned2021-09-07T05:53:52Z
dc.date.available2021-09-07T05:53:52Z
dc.date.issued2021
dc.description.abstractEducational VR may increase engagement and retention compared to traditional learning, for some topics or students. However, a student could still get distracted and disengaged due to stress, mind-wandering, unwanted noise, external alerts, etc. Student eye gaze can be useful for detecting distraction. For example, we previously considered gaze visualizations to help teachers understand student attention to better identify or guide distracted students. However, it is not practical for a teacher to monitor a large numbers of student indicators while teaching. To help filter students based on distraction level, we consider a deep learning approach to detect distraction from gaze data. The key aspects are: (1) we created a labeled eye gaze dataset (3.4M data points) from an educational VR environment, (2) we propose an automatic system to gauge a student's distraction level from gaze data, and (3) we apply and compare three deep neural classifiers for this purpose. A proposed CNN-LSTM classifier achieved an accuracy of 89.8% for classifying distraction, per educational activity section, into one of three levels.en_US
dc.description.sectionheadersVR, Memory, and Cognition
dc.description.seriesinformationICAT-EGVE 2021 - International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual Environments
dc.identifier.doi10.2312/egve.20211326
dc.identifier.isbn978-3-03868-142-7
dc.identifier.issn1727-530X
dc.identifier.pages37-46
dc.identifier.urihttps://doi.org/10.2312/egve.20211326
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egve20211326
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectDeep learning
dc.subjectVirtual reality
dc.subjectApplied computing
dc.subjectEducation
dc.titleDeep Learning on Eye Gaze Data to Classify Student Distraction Level in an Educational VR Environment -- Honorable Mention for Best Paper Awarden_US
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