Pilot experimental study demonstrates automated attention monitoring in classroom students, suggesting low-cost computer vision can track engagement.
This paper presents a new system for automating the monitoring and estimation of student attention during the course session. The followed approach is based on the analysis of the student's gaze to predict his state of attention. A simple hardware device consisting of a camera and a pc was used in this study. Existing machine learning algorithms were used for the student gaze estimation. The principles of homography were used to ensure the transformation from an image coordinates system to a real-world coordinates system. 5 students took part in this experiment and whose gaze was detected and analyzed during 10 minutes of the class session in order to analyze their states of attention and inattention.
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Hachad et al. (2022) studied this question.
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