Abstract
Due to the spread of COVID-19, E-learning has become the only option for the teachers and students. However, it is difficult for a teacher to monitor each student’s engagement while teaching online. This paper aims to develop an automated real-time video-based system to detect student engagement during online classes effortlessly and efficiently. The MobileNet model is trained on an eye images dataset from Kaggle and achieved 99% accuracy on data validation. The result obtained from training the model is used with the Viola–Jones algorithm and OpenCV. By using the built-in camera of the laptop, the system can detect whether the student is engaged or disengaged from his/her eye gaze. In the disengagement state detection, a buzzer sound starts.
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Ahmed, Z.A.T., Jadhav, M.E., Al-madani, A.M., Tawfik, M., Alsubari, S.N., Shareef, A.A.A. (2022). Real-Time Detection of Student Engagement: Deep Learning-Based System. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1387. Springer, Singapore. https://doi.org/10.1007/978-981-16-2594-7_26
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DOI: https://doi.org/10.1007/978-981-16-2594-7_26
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