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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References
Dewan, M. A. A., Murshed, M., & Lin, F. (2019). Engagement detection in online learning: a review. Smart Learning Environments, 6(1), 1.
Z. A. Taha Ahmed and M. E. Jadhav, “A Review of Early Detection of Autism Based on Eye- Tracking and Sensing Technology,” 2020 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 2020, pp. 160-166, https://doi.org/10.1109/icict48043.2020.9112493.
Sharma, P., Joshi, S., Gautam, S., Filipe, V., & Reis, M. J. (2019). Student Engagement De- tection Using Emotion Analysis, Eye Tracking and Head Movement with Machine Learning. arXiv preprint arXiv:1909.12913.
Nezami, O. M., Dras, M., Hamey, L., Richards, D., Wan, S., & Paris, C. (2019, September). Automatic Recognition of Student Engagement using Deep Learning and Facial Expression. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 273-289). Springer, Cham.
Núñez Fernández, D., Barrientos Porras, F., Gilman, R. H., Vittet Mondonedo, M., Sheen, P., & Zimic, M. (2020). A Convolutional Neural Network for gaze preference detection: A po- tential tool for diagnostics of autism spectrum disorder in children. arXiv e-prints, arXiv- 2007.
Rodríguez, C. V., Lavalle, M. M., & Elías, R. P. (2015, November). Modeling student en- gagement by means of nonverbal behavior and Decision trees. In 2015 International Confer- ence on Mechatronics, Electronics and Automotive Engineering (ICMEAE) (pp. 81 -85). IEEE.
Hashemi, M., Mirrashid, A., & Beheshti Shirazi, A. (2020). CNN-based Driver Drowsiness Detection. arXiv, arXiv-2001.
Kaur, A., Mustafa, A., Mehta, L., & Dhall, A. (2018, December). Prediction and localization of student engagement in the wild. In 2018 Digital Image Computing: Techniques and Appli- cations (DICTA) (pp. 1-8). IEEE.
Thomas, C., & Jayagopi, D. B. (2017, November). Predicting student engagement in class- rooms using facial behavioral cues. In Proceedings of the 1st ACM SIGCHI international workshop on multimodal interaction for education (pp. 33-40).
https://www.kaggle.com/abhibasavapattana/eyegaze-classification-using-cnn/data
Chakraborty N., Dan A., Chakraborty A., Neogy S. (2020) Effect of Dropout and Batch Nor- malization in Siamese Network for Face Recognition. In: Khanna A., Gupta D., Bhattacharyya S., Snasel V., Platos J., Hassanien A. (eds) International Conference on Inno- vative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0324-5_3.
g, Y. Q. (2014). An analysis of the Viola-Jones face detection algorithm. Image Processing On Line, 4, 128-148
A. M. Al-madani and A. T. Gaikwad, “IoT Data Security Via Blockchain Technology and Service-Centric Networking,” 2020 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 2020, pp. 17-21, https://doi.org/10.1109/icict48043.2020.9112521.
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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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