Real-Time Emotion Recognition in Online Learning Using Google Teachable

Nazli Rahmeisi

Abstract

Understanding learners’ emotional engagement in e-learning environments remains challenging due to the limited availability of non-verbal cues, despite its importance for motivation and participation. This paper proposes a facial emotion recognition approach using Google's Teachable Machine to support real-time emotion detection within online learning environments. The system analyzes facial expressions captured through a standard webcam to classify four basic emotional states: happy, sad, neutral, and angry. An experimental design was employed using simulated emotional expressions collected under controlled conditions, including adequate lighting and front-facing facial images. The results indicate that the system can provide instructors with additional affective cues to support formative assessment and instructional awareness in synchronous online learning. The proposed approach emphasizes practical instructional feasibility and accessibility compared to more complex emotion recognition models, as it does not require specialized hardware or advanced programming skills.

Keywords

Affective Computing; Emotion Recognition; Facial Expression Analysis; Online Learning; Teachable Machine

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References

Bosch, N. (2016). Detecting student engagement: Human versus machine. Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, 317–320. https://doi.org/10.1145/2930238.2930371

Calvo, R. A., & D’Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1), 18–37. https://doi.org/10.1109/T-AFFC.2010.1

Chen, Y., & He, J. (2022). Deep Learning-Based Emotion Detection. Journal of Computer and Communications, 10(02), 57–71. https://doi.org/10.4236/jcc.2022.102005

D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157. https://doi.org/10.1016/j.learninstruc.2011.10.001

Ding, Y., & Xing, W. (2022). Emotion recognition and achievement prediction for foreign language learners under the background of network teaching. Frontiers in Psychology, 13, 1017570. https://doi.org/10.3389/fpsyg.2022.1017570

Hakim, G. J. P., Simangunsong, G. A., Ningrat, R. W., Rabika, J. C., Rusafni, M. R., Giri, E. P., & Mindara, G. P. (2024). Real-Time Facial Emotion Detection Application with Image Processing Based on Convolutional Neural Network (CNN). International Journal of Electrical Engineering, Mathematics and Computer Science, 1(4), 27–36. https://doi.org/10.62951/ijeemcs.v1i4.123

Kurz, T. L., Jayasuriya, S., Swisher, K., Mativo, J., Pidaparti, R., & Robinson, D. T. (2024). The Impact of Teachable Machine on Middle School Teachers’ Perceptions of Science Lessons after Professional Development. Education Sciences, 14(4), 417. https://doi.org/10.3390/educsci14040417

Li, S., Ji, M., Chen, M., & Chen, L. (2024). Facial length and angle feature recognition for digital libraries. PLoS ONE, 19(7), e0306250. https://doi.org/10.1371/journal.pone.0306250

Llurba, C., & Palau, R. (2024). Real-Time Emotion Recognition for Improving the Teaching–Learning Process: A Scoping Review. Journal of Imaging, 10(12), 313. https://doi.org/10.3390/jimaging10120313

Florestiyanto, M. Y. (2024). Emotion Recognition for Improving Online Learning Environments: A Systematic Review of the Literature. Journal of Electrical Systems, 20(S4), 1860–1873. https://doi.org/10.52783/jes.2255

Pekrun, R. (2024). Control-Value Theory: From Achievement Emotion to a General Theory of Human Emotions. Educational Psychology Review, 36(3), 97. https://doi.org/10.1007/s10648-024-09909-7

Qi, Y., Zhuang, L., Chen, H., Han, X., & Liang, A. (2024). Evaluation of Students’ Learning Engagement in Online Classes Based on Multimodal Vision Perspective. Electronics, 13(1), 149. https://doi.org/10.3390/electronics13010149

Rathod, M., Dalvi, C., Kaur, K., Patil, S., Gite, S., Kamat, P., Kotecha, K., Abraham, A., & Gabralla, L. A. (2022). Kids’ Emotion Recognition Using Various Deep-Learning Models with Explainable AI. Sensors, 22(20), 8066. https://doi.org/10.3390/s22208066

Salloum, S. A., Alomari, K. M., Alfaisal, A. M., Aljanada, R. A., & Basiouni, A. (2025). Emotion recognition for enhanced learning: using AI to detect students’ emotions and adjust teaching methods. Smart Learning Environments, 12(1), 1–25. https://doi.org/10.1186/s40561-025-00374-5

Savchenko, A. V. (2021). Facial expression and attributes recognition based on multi-task learning of lightweight neural networks. 2021 IEEE 19th International Symposium on Intelligent Systems and Informatics (SISY), 119–124. https://doi.org/10.1109/SISY52375.2021.9582508

Ye, F. (2022). Emotion Recognition of Online Education Learners by Convolutional Neural Networks. Computational Intelligence and Neuroscience, 2022, 4316812. https://doi.org/10.1155/2022/4316812

Yuvaraj, R., Mittal, R., Prince, A. A., & Huang, J. S. (2025). Affective Computing for Learning in Education: A Systematic Review and Bibliometric Analysis. Education Sciences, 15(1), 65. https://doi.org/10.3390/educsci15010065

Zhou, Q. (2023). Application of AI Technology in Online Platforms Based on Cognitive Emotion Regulation. Frontiers in Artificial Intelligence and Applications, 378, 262–271. https://doi.org/10.3233/FAIA231030

DOI: https://doi.org/10.20961/ijie.v9i2.110565