![]() There is a prominent increase in hand-over-face gestures when the difficulty level of the given exercise increases. We propose a novel deep learning approach for automatic detection of hand-over-face gestures in images with a classification accuracy of 86.87%. We demonstrated that there is a considerable occurrence of hand-over-face gestures (on average 21.35%) during the 40 minutes session and is unexplored in the education domain. We found that there is a significant increase in head and eye movements as time progresses, as well as with the increase of difficulty level. The exercises in the sessions are divided into three categories: an easy, medium and difficult topic within the context of undergraduate computer science. We investigate these behaviors in-depth over time in a classroom session of 40 minutes involving reading and problem-solving exercises. The proposed computer vision-based behavior monitoring method uses a low-cost webcam and can easily be integrated with modern tutoring technologies. In this paper, we explore the automatic detection of learner’s nonverbal behaviors involving hand-over-face gestures, head and eye movements and emotions via facial expressions during learning. Therefore, recognizing and understanding these states in the context of learning is key in designing informed interventions and addressing the needs of the individual student to provide personalized education. ![]() ![]() ![]() Learning involves a substantial amount of cognitive, social and emotional states. ![]()
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March 2023
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