IMPROVING E-LEARNING BY FACIAL EXPRESSION ANALYSIS
Amina KINANE DAOUADJI
a.dkinane@gmail.coma:1:{s:5:"en_US";s:142:"Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf, Faculté Des Mathématiques Et Informatique, Département d'informatique";} (Algeria)
Fatima BENDELLA
Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf, Faculté Des Mathématiques Et Informatique, Département d'informatique (Algeria)
Abstract
Modern technology has become a vital part of our daily lives, and the world has undergone remarkable advancements in various scientific and technological fields. The advancement of technology presents a variety of opportunities for students to promote academic development and make it easier to access education through online learning systems. The most difficult and most demanding task during learning is to be aware of and support the emotional side of students. Recognizing one's emotions is easy for humans, but it is a challenging task for computers due to the specific features of the human face. However, recent advances in computing and image processing have made it possible and easy to detect and categorize emotions in images and videos. This paper focuses on detecting learners' emotions in real time during synchronous learning. In this regard, a video/chat application has been developed for the tutor to detect the emotions of the learners while presenting his lesson. The emotions detected are separated into three states (Satisfied, Neutral and Unsatisfied); each state is made up of two or three distinct emotions. The objective is to assist teachers in adapting teaching methods in virtual learning settings according to the emotions of learners.
Keywords:
Convolutional neural network, facial expression analysis, classification, E-learning, DeeplearningReferences
Al-Hazaimeh, O. M., & Al-Smadi, M. (2019). Automated pedestrian recognition based on deep convolutional Neural Networks. International Journal of Machine Learning and Computing, 9(5), 662‑667. https://doi.org/10.18178/ijmlc.2019.9.5.855
DOI: https://doi.org/10.18178/ijmlc.2019.9.5.855
Google Scholar
Azcarate, A., Hageloh, F., Sande, K., & Valenti, R. (2005). Automatic facial emotion recognition. Universiteit van Amsterdam.
Google Scholar
Benadla, D., & Hadji, M. (2021). EFL Students Affective Attitudes towards Distance E-Learning Based on Moodle Platform during the Covid-19the Pandemic : Perspectives from Dr. MoulayTahar University of Saida, Algeria. Arab World English Journal, 55-67. https://doi.org/10.31235/osf.io/4xepz
DOI: https://doi.org/10.24093/awej/covid.4
Google Scholar
Budhwar, K. (2017). The role of technology in education. International Journal of Engineering Applied Sciences and Technology, 2(8), 55‑57.
Google Scholar
Chandrakala, P., Srinivas, B., & Anil, K. M. (2022). Real time face detection and face recognition using OpenCV and Python. Journal of Engineering Sciences, 13(06), 696‑706.
Google Scholar
Dhawan, S. (2020). Online learning : A Panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5‑22. https://doi.org/10.1177/0047239520934018
DOI: https://doi.org/10.1177/0047239520934018
Google Scholar
Elliott, E. A., & Jacobs, A. M. (2013). Facial expressions, emotions, and sign languages. Frontiers in Psychology, 4, 115. https://doi.org/10.3389/fpsyg.2013.00115
DOI: https://doi.org/10.3389/fpsyg.2013.00115
Google Scholar
Engelbrecht, E. (2005). Adapting to changing expectations : Post-graduate students’ experience of an e-learning tax program. Computers & Education, 45(2), 217‑229. https://doi.org/10.1016/j.compedu.2004.08.001
DOI: https://doi.org/10.1016/j.compedu.2004.08.001
Google Scholar
Farkhod, A., Abdusalomov, A. B., Mukhiddinov, M., & Cho, Y.-I. (2022). Development of real-time landmark-based emotion recognition CNN for masked faces. Sensors, 22(22), 8704. https://doi.org/10.3390/s22228704
DOI: https://doi.org/10.3390/s22228704
Google Scholar
Garcia-Garcia, J. M., Penichet, V. M. R., & Lozano, M. D. (2017). Emotion detection : A technology review. Proceedings of the XVIII International Conference on Human Computer Interaction (pp. 1‑8). https://doi.org/10.1145/3123818.3123852
DOI: https://doi.org/10.1145/3123818.3123852
Google Scholar
Gray, J. A., & DiLoreto, M. (2016). The effects of student engagement, student satisfaction, and perceived learning in online learning environments. International Journal of Educational Leadership Preparation, 11(1).
Google Scholar
Harandi, S. R. (2015). Effects of e-learning on students’ motivation. Procedia - Social and Behavioral Sciences, 181, 423‑430. https://doi.org/10.1016/j.sbspro.2015.04.905
DOI: https://doi.org/10.1016/j.sbspro.2015.04.905
Google Scholar
Heredia, J., Lopes-Silva, E., Cardinale, Y., Diaz-Amado, J., Dongo, I., Graterol, W., & Aguilera, A. (2022). Adaptive multimodal emotion detection architecture for social robots. IEEE Access, 10, 20727‑20744. https://doi.org/10.1109/ACCESS.2022.3149214
DOI: https://doi.org/10.1109/ACCESS.2022.3149214
Google Scholar
Hussain, S. A., & Salim Abdallah Al Balushi, A. (2020). A real time face emotion classification and recognition using deep learning model. Journal of Physics: Conference Series, 1432, 012087. https://doi.org/10.1088/1742-6596/1432/1/012087
DOI: https://doi.org/10.1088/1742-6596/1432/1/012087
Google Scholar
Keshri, A., Singh, A., Kumar, B., Pratap, D., & Chauhan, A. (2022). Automatic detection and classification of human emotion in real-time scenario. Journal of IoT in Social, Mobile, Analytics, and Cloud, 4(1), 5. https://doi.org/10.36548/jismac.2022.1.005
DOI: https://doi.org/10.36548/jismac.2022.1.005
Google Scholar
Kumar, A., Kaur, A., & Kumar, M. (2019). Face detection techniques : A review. Artificial Intelligence Review, 52, 927‑948. https://doi.org/10.1007/s10462-018-9650-2
DOI: https://doi.org/10.1007/s10462-018-9650-2
Google Scholar
Mahanta, D., & Ahmed, M. (2012). E-Learning objectives, methodologies, tools and its limitation. International Journal of Innovative Technology and Exploring Engineering, 2(1), 46-51.
Google Scholar
Memari, M. (2020). Synchronous and asynchronous electronic learning and EFL learners’ learning of grammar. Iranian Journal of Applied Language Studies, 12(2), 89‑114. https://doi.org/10.22111/ijals.2020.6043
Google Scholar
Muhammad, N., Ariyanto, E., & Yudo, Y. (2023). Improved face detection accuracy using Haar cascade classifier method and ESP32-CAM for IoT-based home door security. Jurnal Ilmiah Penelitian dan Pembelajaran Informatika, 8(1), 154‑161. https://doi.org/10.29100/jipi.v8i1.3365
DOI: https://doi.org/10.29100/jipi.v8i1.3365
Google Scholar
Perwej, Y., Trivedi, A., Tripathi, C., Srivastava, A., & Kulshrestha, N. (2022). Face recognition based automated attendance management system. International Journal of Scientific Research in Science and Technology, 9(1), 261-268. https://doi.org/10.32628/IJSRST229147
DOI: https://doi.org/10.32628/IJSRST229147
Google Scholar
Rizvi, Q. M., Agarwal, B. G., & Beg, R. (2011). A Review on face detection methods. Journal of Management Development and Information Technology, 11.
Google Scholar
Sati, V., Sánchez, S. M., Shoeibi, N., Arora, A., & Corchado, J. M. (2021). Face detection and recognition, face emotion recognition through NVIDIA Jetson Nano. In P. Novais, G. Vercelli, J. L. Larriba-Pey, F. Herrera, & P. Chamoso (Eds.), Advances in Intelligent Systems and Computing (pp. 177‑185). Springer International Publishing. https://doi.org/10.1007/978-3-030-58356-9_18
DOI: https://doi.org/10.1007/978-3-030-58356-9_18
Google Scholar
Schmidt, K. L., & Cohn, J. F. (2001). Human facial expressions as adaptations: Evolutionary questions in facial expression research. American journal of physical anthropology, 33, 3‑24. https://doi.org/10.1002/ajpa.2001
DOI: https://doi.org/10.1002/ajpa.20001
Google Scholar
Seidel, E.-M., Habel, U., Kirschner, M., Gur, R. C., & Derntl, B. (2010). The impact of facial emotional expressions on behavioral tendencies in women and men. Journal of Experimental Psychology. Human Perception and Performance, 36(2), 500‑507. https://doi.org/10.1037/a0018169
DOI: https://doi.org/10.1037/a0018169
Google Scholar
Singh, R., & Awasthi, S. (2020). Updated comparative analysis on video conferencing platforms - Zoom, Google Meet, Microsoft Teams, WebEx Teams and GoToMeetings. EasyChair Preprint, 4026. https://easychair.org/publications/preprint/Fq7T
Google Scholar
Sridharan, M., Arulanandam, D. C. R., Chinnasamy, R. K., Thimmanna, S., & Dhandapani, S. (2021). Recognition of font and tamil letter in images using deep learning. Applied Computer Science, 17(2), 90‑99. https://doi.org/10.23743/acs-2021-15
DOI: https://doi.org/10.35784/acs-2021-15
Google Scholar
Tarnowski, P., Kołodziej, M., Majkowski, A., & Rak, R. J. (2017). Emotion recognition using facial expressions. Procedia Computer Science, 108, 1175‑1184. https://doi.org/10.1016/j.procs.2017.05.025
DOI: https://doi.org/10.1016/j.procs.2017.05.025
Google Scholar
Tian, Y., Kanade, T., & Cohn, J. F. (2011). Facial expression recognition. In S. Z. Li & A. K. Jain (Eds.), Handbook of Face Recognition (pp. 487–519). Springer London. https://doi.org/10.1007/978-0-85729-932-1_19
DOI: https://doi.org/10.1007/978-0-85729-932-1_19
Google Scholar
Yücelsin-Taş, Y. T. (2021). Difficulties encountered by students during distance education in times of confinement in Turkey. Educational Research and Reviews, 16(3), 87-92.
Google Scholar
Authors
Amina KINANE DAOUADJIa.dkinane@gmail.com
a:1:{s:5:"en_US";s:142:"Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf, Faculté Des Mathématiques Et Informatique, Département d'informatique";} Algeria
Authors
Fatima BENDELLAUniversité des Sciences et de la Technologie d'Oran Mohamed Boudiaf, Faculté Des Mathématiques Et Informatique, Département d'informatique Algeria
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