Enhancing Online Education Through Machine Learning: A Comprehensive Review And Future Directions
Abstract
Online learning environments are gaining popularity as an alternative to traditional learning environments during times of global pandemic. The traditional classroom setting has given way to online learning in recent educational developments. To employ it as a classifier in online education, the goal of this work is to construct a real-time face emotion detection system that recognizes and categorizes emotions of a human. It uses machine learning to identify, forecast, and analyse a learner's facial expressions, and it further maps those expressions to a learning affect that categorizes the emotions of those who are seen on camera. Academic feelings can have a significant impact on learning outcomes. Students typically show their emotions through their facial expressions, voice, and behavior. A spontaneous facial expression database is created in light of the inference algorithm's lack of training samples. It consists of two subsets: a video clip database and a picture database, and it includes the typical emotional facial expressions. The database contains 1,274 video clips and 30,184 photos from 82 pupils. The parameters for student facial detection can be used to gauge each learner's rate of concentration. The performances of the SVM and RF classifier in facial expression for the image are presented. The results of testing this with the RF algorithm and a 90.14% accuracy rate produced extremely good results.
References
2)Junge, S., Haopeng, Y., Jiawei, L., and Zhiyong, C. (2021). Assessing learning engagement based on facial expression recognition in MOOC’s scenario. Springer, 1-10.
3) Leghris and R. Mrabet, “Cost Comparison of E-Learning Solutions," 2006 7th International Conference on Information Technology Based Higher Education and Training, Sydney, NSW, 2006, pp. 817-824.
4) Fresen, J. (2007). Taxonomy of factors to promote quality websupported learning. International Journal on E-Learning, 6(3)
5) Tofighi, M., Guo, T., Vanamala, J.K.P., Monga, V.: Prior information guided regularized deep learning for cell nucleus detection. IEEE Trans. Med. Imaging 38, 2047–2058 (2019)
6) Kumar, K., Rao, A.C.S.: Breast cancer classification of image using convolutional neural networks. In: 4th International Conference on Recent Advances in Information Technology (RAIT), Dhanbad, India (2018)
7) Redmond, P. (2011). From face-to-face teaching to online teaching: Pedagogical transitions. In B. Williams, G. Statham, P. Brown, & N. Cleland (Eds.), ASCILITE Hobart 2011 (pp. 1050–1060)
8) Mori, Makoto. "Identifying Student Behavior for Improving Online Course Performance with Machine Learning." PhD diss., 2015.
9) Lin, J. M., Wang, P., & Lin, I. (2012). Pedagogy* technology: A two-dimensional model for teachers’ ICT integration. British Journal of Educational Technology, 43(1), 97–108. https://doi. org/10.1111/j.1467-8535.2010.01159. 10) Kilgour, P., Reynaud, D., Northcote, M., Mcloughlin, C., Gosselin, K. P., Kilgour, P.Mcloughlin, C. (2018).
11) G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955. (references)
12) M. Nur-Awaleh and L. Kyei-Blankson, "Assessing E-learning and student satisfaction in a blended and flexible environment,” 2010 International Conference on Information Society, London, 2010, pp. 481-483.
13) Guey-Shya Chen and Min-Feng Lee, “Detecting emotion model in elearning system,” 2012 International Conference on Machine Learning and Cybernetics, Xian, 2012, pp. 1686-1691.
14) Zotou, Maria, Efthimios Tambouris, and Konstantinos Tarabanis. "Data-driven problem-based learning: Enhancing problem-based learning with learning analytics." Educational Technology Research and Development 68, no. 6 (2020): 3393-3424.
15) S. Li and W. Deng, “Deep Facial Expression Recognition: A Survey,” IEEE Trans. on Affective Computing, doi: 10.1109/TAFFC.2020.2981446, 202
16) Yusra, K. B., Afshan, J., Nudrat, N., Muhammad, H. Y., Serestina, V., and Sergio, A. V. (2021). Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine. Hindawi Computational Intelligence and Neuroscience, 1-17.
17) Hanusha, T., and Varalatchoumy. (2021). Facial Emotion Recognition System using Deep Learning and Convolutional Neural Networks. International Journal of Engineering Research & Technology (IJERT), 803-811.
18) Nazia, P., Nazir, A., M Abdul, Q. B. K., Rizwan, K., Salman, Q. (2016). Facial Expression Recognition Through Machine Learning, International Journal of Scientific and Technology Research, 5(3), 91 -97.
19) Turabzadeh, S., Meng, H., Swash, R.M., Pleya, M., Juhar, J.: 'Facial expression emotion detection for real-time embedded systems', Technologies, 2018, 6, (1), 17
20) Bidwell, J., Fuchs, H.: 'Classroom analytics: Measuring student engagement with automated gaze tracking', Behav Res Methods, 2011, 49:113
21) Krithika, L.B.: ' Student emotion recognition system (SERS) for e -learning improvement based on learner concentration metric ', Procedia Computer Science, 2016, 85, pp. 767 –776
22) Sharma, P., Esengönül, M., Khanal, S.R., Khanal, T.T., Filipe, V., Reis, M.J.C.S.: ‘Student Concentration Evaluation Index in an E -learning Context Using Facial Emotion Analysis’, Tsitouridou M., A. Diniz J., Mikropoulos T. (eds) Technology and Innovation in Learning, Teaching and Education, TECH -EDU 2018, pp. 529 -538
23) G. Yang, J. S. Y. Ortoneda and J. Saniie, “Emotion Recognition Using Deep Neural Network with Vectorized Facial Features,” in IEEE International Conference on Electro/Information Technology (EIT), Rochester, Michigan, USA, 2018.
24) S. Zhang, X. Pan, Y. Cui, X. Zhao and L. Liu, “Learning Affective Video Features for Facial Expression Recognition via Hybrid Deep Learning,” IEEE Access, vol. 7, pp. 32297- 32304, 2019.
25) H. W. Marsh, “Students’ evaluations of university teaching: Dimensionality, reliability, validity, potential biases and usefulness,” in De Scholarship of Teaching and Learning in Higher Education: An Evidence-Based Perspective, pp. 319–383, Springer, Berlin, Germany, 2007
26) S. Aslan, N. Alyuz, C. Tanriover et al., “Investigating the impact of a real-time, multimodal student engagement analytics technology in authentic classrooms,” in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–12, Scotland, UK, May 2019
