Current - Issue
Original Article
Student Engagement Monitoring using MobileNetV2 Model
Dalbina Dalan1
Dr. M. Sengaliappan2
1 Ph.D. Scholar, Department of MCA, Nehru College of Management, Bharathiar University, Coimbatore, Tamilnadu, India. 2 Professor, Department of MCA, Nehru College of Management, Bharathiar University, Coimbatore, Tamilnadu, India.
Published Online: May-August 2026
Pages: 706-714
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502081
References
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1–20. https://doi.org/10.1186/s40561-018-0080-z
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Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2018.00474
11. Shiri, F. M., Ahmadi, E., Rezaee, M. R., & Perumal, T. (2024). Detection of student engagement in e-learning environments using
EfficientNetV2-L together with RNN-based models. Journal of Artificial Intelligence. https://doi.org/10.32604/jai.2024.04891112. Zhang, Z., et al. (2020). Data-driven online learning engagement detection via facial expression and mouse behavior recognition technology.
Journal of Educational Computing Research, 58(1), 63–86. https://doi.org/10.1177/0735633119825575
13. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
https://doi.org/10.1038/nature14539
14. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Communications of
theACM,60(6),84–90. https://doi.org/10.1145/3065386
15. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. CVPR. https://doi.org/10.1109/CVPR.2016.90
16. Howard, A. G., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications.
https://arxiv.org/abs/1704.04861
17. Tan, M., & Le, Q. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. ICML. https://arxiv.org/abs/1905.11946
18. Li, S., & Deng, W. (2020). Deep facial expression recognition: A survey. IEEE Transactions on Affective Computing.
https://doi.org/10.1109/TAFFC.2020.2981446
19. Ko, B. C. (2018). A brief review of facial emotion recognition based on visual information. Sensors, 18(2), 401.
https://doi.org/10.3390/s18020401
20. Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2017). AffectNet: A database for facial expression recognition. IEEE Transactions on
AffectiveComputing. https://doi.org/10.1109/TAFFC.2017.2740923
https://doi.org/10.1016/j.learninstruc.2011.10.001
2. Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of
Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059
3. Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review.
Computers & Education, 90, 36–53. https://doi.org/10.1016/j.compedu.2015.09.005
4. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2018.00474
5. Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
https://doi.org/10.1109/TKDE.2009.191
6. Anh, B. N., Son, N. T., Lam, P. T., Chi, L. P., Tuan, N. H., Dat, N. C., Trung, N. H., Aftab, M. U., & Dinh, T. V. (2019). A computer-vision
based application for student behavior monitoring in classroom. Applied Sciences, 9(22), 4729. https://doi.org/10.3390/app9224729
7. Dewan, M. A. A., Murshed, M., & Lin, F. (2019). Engagement detection in online learning: A review. Smart Learning Environments, 6(1),
1–20. https://doi.org/10.1186/s40561-018-0080-z
8. Fakhar, S., Baber, J., Bazai, S. U., Marjan, S., Jasinski, M., Jasinska, E., Chaudhry, M. U., Leonowicz, Z., & Hussain, S. (2022). Smart
classroom monitoring using novel real-time facial expression recognition system. Applied Sciences, 12(23), 12134.
https://doi.org/10.3390/app122312134
9. Lin, F.-C., Ngo, H.-H., Dow, C.-R., Lam, K.-H., & Le, H. L. (2021). Student behavior recognition system for the classroom environment
based on skeleton pose estimation and person detection. Sensors, 21(16), 5314. https://doi.org/10.3390/s21165314
10. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2018.00474
11. Shiri, F. M., Ahmadi, E., Rezaee, M. R., & Perumal, T. (2024). Detection of student engagement in e-learning environments using
EfficientNetV2-L together with RNN-based models. Journal of Artificial Intelligence. https://doi.org/10.32604/jai.2024.04891112. Zhang, Z., et al. (2020). Data-driven online learning engagement detection via facial expression and mouse behavior recognition technology.
Journal of Educational Computing Research, 58(1), 63–86. https://doi.org/10.1177/0735633119825575
13. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
https://doi.org/10.1038/nature14539
14. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Communications of
theACM,60(6),84–90. https://doi.org/10.1145/3065386
15. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. CVPR. https://doi.org/10.1109/CVPR.2016.90
16. Howard, A. G., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications.
https://arxiv.org/abs/1704.04861
17. Tan, M., & Le, Q. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. ICML. https://arxiv.org/abs/1905.11946
18. Li, S., & Deng, W. (2020). Deep facial expression recognition: A survey. IEEE Transactions on Affective Computing.
https://doi.org/10.1109/TAFFC.2020.2981446
19. Ko, B. C. (2018). A brief review of facial emotion recognition based on visual information. Sensors, 18(2), 401.
https://doi.org/10.3390/s18020401
20. Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2017). AffectNet: A database for facial expression recognition. IEEE Transactions on
AffectiveComputing. https://doi.org/10.1109/TAFFC.2017.2740923
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