Print Email Facebook Twitter Tracking Sustained Attention with Webcam-Based Eye Gaze and Blink Tracking Title Tracking Sustained Attention with Webcam-Based Eye Gaze and Blink Tracking Author van der Voort, Sven (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Lee, Y. (mentor) Specht, M.M. (graduation committee) Migut, M.A. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2021-07-01 Abstract Sustained attention is a cognitive state where the learners’ attention is completely focused on the learning environment and content-related thoughts for a continuous stretch of time. Sustained attention is vital to perform well on learning tasks, such as reading. Learning analytics platforms that detect changes in sustained attention can prevent ineffective learning by providing direct feedback to the learner. Prior research has found that eye gaze and blink patterns can be good indicators of cognitive state. In this paper we investigate the following main research question: "How can webcam-based eye gaze and blink pattern tracking indicate changes in learners' sustained attention in the remote learning context?". While other studies rely on expensive eye trackers to perform detection, this research explores the use of widely used laptop webcams for detecting changes in sustained attention. We collected webcam data through a small case study involving several different reading tasks. A machine learning classification model was trained on the collected webcam data. The resulting detection model performs well on validation data with a F1-score of 0.889. The model does not perform well on testing data however, showing that it is not usable in practice. We give several possible explanations for this behavior, most of them originating from an overfitted model due to the small size of the user study. Our findings indicate that future work should focus on different experimental settings and larger user studies. Subject multimodallearning analyticsgaze trackingblink trackingwebcam gaze tracking To reference this document use: http://resolver.tudelft.nl/uuid:56ff42f2-2538-49e1-b022-894b15c7956d Bibliographical note https://github.com/MultimodalLearningAnalytics Collection of Git repositories containing all code and generated datasets used during the research. Part of collection Student theses Document type bachelor thesis Rights © 2021 Sven van der Voort Files PDF research_paper_Sven_van_d ... _email.pdf 3.56 MB Close viewer /islandora/object/uuid:56ff42f2-2538-49e1-b022-894b15c7956d/datastream/OBJ/view