Improving e-learning by facial expression analysis

• Autorzy: Daouadji Amina Kinane , Bendella Fatima

Identyfikatory

DOI

10.35784/acs-2024-20

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

convolutional neural network; facial expression analysis; classification; e-learning; deep learning

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2024
• Tom: Vol. 20, no. 2
• Strony: 126--137
• Opis fizyczny: Bibliogr. 29 poz., fig., tab.

Twórcy

autor

Daouadji Amina Kinane

• Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf, Faculté Des Mathématiques Et Informatique, Département d'informatique, Algerie

• https://orcid.org/0000-0002-1897-1900

autor

Bendella Fatima

• Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf, Faculté Des Mathématiques Et Informatique, Département d'informatique, Algerie

• https://orcid.org/0000-0002-7568-5177

Bibliografia

• [1] 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
• [2] Azcarate, A., Hageloh, F., Sande, K., & Valenti, R. (2005). Automatic facial emotion recognition. Universiteit van Amsterdam.
• [3] 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
• [4] Budhwar, K. (2017). The role of technology in education. International Journal of Engineering Applied Sciences and Technology, 2(8), 55‑57.
• [5] 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.
• [6] 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
• [7] 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
• [8] 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
• [9] 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
• [10] 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
• [11] 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).
• [12] 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
• [13] 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
• [14] 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
• [15] 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
• [16] 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
• [17] 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.
• [18] 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
• [19] 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
• [20] 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
• [21] Rizvi, Q. M., Agarwal, B. G., & Beg, R. (2011). A Review on face detection methods. Journal of Management Development and Information Technology, 11.
• [22] 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
• [23] 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
• [24] 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
• [25] 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
• [26] 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
• [27] 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
• [28] 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
• [29] 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.