Tracking Sustained Attention with Webcam-Based Eye Gaze and Blink Tracking

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.