Deep Learning Based Facial Emotion Recognition for Analysing the Effectiveness of Online Class


  • Sophiya Mathews, D. John Aravindhar


Mobile Netv2, Online class, Fine tuning, Performance metric, ImageNet Dataset


Online classes break down barriers of distance and time, allowing students from different geographical locations and backgrounds to access quality education. However, monitoring student engagement and emotional well-being during online classes presents a unique challenge. This study aims to analyze the facial expressions of students during online classes, in order to assess their emotional states and evaluate the performance of a fine-tuned MobileNet V2 architecture. To conduct this study, we utilized the CK+ dataset, which consists of labeled facial expressions captured in controlled laboratory settings. To specifically identify the emotions shown by students during online classes, the MobileNet V2 model is first pre-trained on ImageNet, a large-scale picture classification dataset, and then refined on the CK+ dataset. Preprocessing techniques such as image augmentation and normalization are applied to enhance the model's generalization capability.  Before fine-tuning, the pre-trained model achieved moderate level of performance. After fine-tuning, the performance of the model achieved higher accuracy of 98.40% compared to the base model, indicating its enhanced ability to detect and classify facial emotions during online classes. By leveraging deep learning-based tools like the proposed model, educators can gain valuable real-time feedback on the effectiveness of their online teaching methods and make data-driven decisions to optimize the learning experience for their students.


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Blossfeld, H. P., & Von Maurice, J. (2019). Education as a lifelong process (pp. 17-33). Springer Fachmedien Wiesbaden.

Debognies, P., Schaillée, H., Haudenhuyse, R., & Theeboom, M. (2019). Personal development of disadvantaged youth through community sports: A theory-driven analysis of relational strategies. Sport in Society, 22(6), 897-918.

Aly, M., Audretsch, D. B., & Grimm, H. (2021). Emotional skills for entrepreneurial success: the promise of entrepreneurship education and policy. The Journal of Technology Transfer, 46(5), 1611-1629.

Hultsjö, S., Bachrach-Lindström, M., Safipour, J., & Hadziabdic, E. (2019). “Cultural awareness requires more than theoretical education”-Nursing students’ experiences. Nurse Education in Practice, 39, 73-79.

Kraft, M. A. (2020). Interpreting effect sizes of education interventions. Educational Researcher, 49(4), 241-253.

Smith, W. C., & Benavot, A. (2019). Improving accountability in education: the importance of structured democratic voice. Asia Pacific Education Review, 20, 193-205.

Meerow, S., Pajouhesh, P., & Miller, T. R. (2019). Social equity in urban resilience planning. Local Environment, 24(9), 793-808.

Elenbaas, L., Rizzo, M. T., & Killen, M. (2020). A developmental-science perspective on social inequality. Current Directions in Psychological Science, 29(6), 610-616.

Sergi, B. S., Popkova, E. G., Bogoviz, A. V., & Ragulina, J. V. (2019). Entrepreneurship and economic growth: the experience of developed and developing countries. In Entrepreneurship and Development in the 21st Century (pp. 3-32). Emerald publishing limited.

Darby, F., & Lang, J. M. (2019). Small teaching online: Applying learning science in online classes. John Wiley & Sons.

Wei, H. C., & Chou, C. (2020). Online learning performance and satisfaction: do perceptions and readiness matter?. Distance Education, 41(1), 48-69.

Afrouz, R., & Crisp, B. R. (2021). Online education in social work, effectiveness, benefits, and challenges: A scoping review. Australian Social Work, 74(1), 55-67.

Farrell, W., & Pattermann, J. (2022). The Role of Online Formative Assessment in Higher Education: Effectiveness and Student Satisfaction. In Zukunft verantwortungsvoll gestalten: Forschungsforum der österreichischen Fachhochschulen 2021 (pp. 141-155). Wiesbaden: Springer Fachmedien Wiesbaden.

Mostofa, S. M., Hossain, M. U., Othman, R., Hasan, K. K., & Rahman, M. K. (2022, July). Student Perception on Knowledge Management: Effectiveness of Online Learning During the Pandemic. In Innovation of Businesses, and Digitalization during Covid-19 Pandemic: Proceedings of The International Conference on Business and Technology (ICBT 2021) (pp. 889-905). Cham: Springer International Publishing.

AlAteeq, D. A., Aljhani, S., & AlEesa, D. (2020). Perceived stress among students in virtual classrooms during the COVID-19 outbreak in KSA. Journal of Taibah University Medical Sciences, 15(5), 398-403.

Choudhury, P., Foroughi, C., & Larson, B. (2021). Work‐from‐anywhere: The productivity effects of geographic flexibility. Strategic Management Journal, 42(4), 655-683.

Torres Martín, C., Acal, C., El Homrani, M., & Mingorance Estrada, Á. C. (2021). Impact on the virtual learning environment due to COVID-19. Sustainability, 13(2), 582.

Rivas, A., Gonzalez-Briones, A., Hernandez, G., Prieto, J., & Chamoso, P. (2021). Artificial neural network analysis of the academic performance of students in virtual learning environments. Neurocomputing, 423, 713-720.

Yulia, H. (2020). Online learning to prevent the spread of pandemic corona virus in Indonesia. ETERNAL (English Teaching Journal), 11(1).

Adnan, M., & Anwar, K. (2020). Online Learning amid the COVID-19 Pandemic: Students' Perspectives. Online Submission, 2(1), 45-51.

Batdı, V., Doğan, Y., & Talan, T. (2021). Effectiveness of online learning: a multi-complementary approach research with responses from the COVID-19 pandemic period. Interactive Learning Environments, 1-34.

Arhas, S. H., Mahardika, L. A., & Zainuddin, M. S. (2022). Effectiveness of Online System Lectures during the Covid-19 Pandemic. Pinisi Journal of Education and Management, 1(2), 127-134.

Jhawar, N., & Nandedkar, T. (2022). Effectiveness of Online Teaching Learning Process. Online ISSN: 0976-173X, 141.

Nordin, N. (2021). The effectiveness of online-based learning in java programming language: student perceptions and performance. Journal of Technology and Operations Management, 15(1), 1-24.

Al-Maroof, R. S., Alnazzawi, N., Akour, I. A., Ayoubi, K., Alhumaid, K., AlAhbabi, N. M., ... & Salloum, S. (2021, December). The effectiveness of online platforms after the pandemic: Will face-to-face classes affect students’ perception of their behavioural intention (BIU) to use online platforms?. In Informatics (Vol. 8, No. 4, p. 83). Multidisciplinary Digital Publishing Institute.

Chang, J. Y. F., Wang, L. H., Lin, T. C., Cheng, F. C., & Chiang, C. P. (2021). Comparison of learning effectiveness between physical classroom and online learning for dental education during the COVID-19 pandemic. Journal of dental sciences, 16(4), 1281-1289.

Smith, Y., Chen, Y. J., & Warner-Stidham, A. (2021). Understanding online teaching effectiveness: Nursing student and faculty perspectives. Journal of Professional Nursing, 37(5), 785-794.

Al-Amin, M., Al Zubayer, A., Deb, B., & Hasan, M. (2021). Status of tertiary level online class in Bangladesh: students’ response on preparedness, participation and classroom activities. Heliyon, 7(1), e05943.

Muthuprasad, T., Aiswarya, S., Aditya, K. S., & Jha, G. K. (2021). Students’ perception and preference for online education in India during COVID-19 pandemic. Social sciences & humanities open, 3(1), 100101.

Şahin, F., Doğan, E., İlic, U., & Şahin, Y. L. (2021). Factors influencing instructors’ intentions to use information technologies in higher education amid the pandemic. Education and Information Technologies, 26, 4795-4820.

Farahat, T. (2012). Applying the technology acceptance model to online learning in the Egyptian universities. Procedia-Social and Behavioral Sciences, 64, 95-104.

Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057-7077.

Zhou, L., Xue, S., & Li, R. (2022). Extending the Technology Acceptance Model to explore students’ intention to use an online education platform at a University in China. Sage Open, 12(1), 21582440221085259.

Maheshwari, G. (2021). Factors affecting students’ intentions to undertake online learning: an empirical study in Vietnam. Education and Information Technologies, 26(6), 6629-6649.

Renda, A., Frankle, J., & Carbin, M. (2020). Comparing rewinding and fine-tuning in neural network pruning. arXiv preprint arXiv:2003.02389.

Chlap, P., Min, H., Vandenberg, N., Dowling, J., Holloway, L., & Haworth, A. (2021). A review of medical image data augmentation techniques for deep learning applications. Journal of Medical Imaging and Radiation Oncology, 65(5), 545-563.

Li, X., Zhang, W., Ding, Q., & Sun, J. Q. (2020). Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation. Journal of Intelligent Manufacturing, 31, 433-452.

Agarap, A. F. (2018). Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375.




How to Cite

D. John Aravindhar, S. M. . (2024). Deep Learning Based Facial Emotion Recognition for Analysing the Effectiveness of Online Class. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1342–1350. Retrieved from



Research Article