Facial Expression Recognition for Monitoring Learning Satisfaction in Smart Learning Environments Using MobileNetV2
DOI:
10.33395/sinkron.v10i1.15565Keywords:
FER, MobileNetV2, SLE, LSI, Edge-computingAbstract
This study develops a lightweight, privacy-aware Facial Expression Recognition (FER) framework to monitor learning satisfaction in Smart Learning Environments (SLEs). Using MobileNetV2 with a two-stage training scheme on the FER2013 dataset and evaluated on 35,000 test samples, the system addresses two main questions: (1) how effectively a customized MobileNetV2 recognizes core student expressions under authentic classroom conditions, and (2) how temporal aggregation and confidence calibration improve the stability of a Learning Satisfaction Index (LSI). The model achieves 0.39 accuracy and 0.34 macro-F1, with strong performance for happy, neutral, and surprise, while challenges remain for fear–surprise and neutral–sad. Temporal smoothing reduces prediction noise and enhances the reliability of LSI signals for instructional decision-making. The findings highlight practical implications for education, particularly in supporting real-time formative assessment and improving teachers’ awareness of student engagement through privacy-preserving, on-device affect monitoring.
Downloads
References
Hindarto, D. (2023a). Comparative Analysis VGG16 Vs MobileNet Performance for Fish Identification. 3(December), 270–280.
Hindarto, D. (2023b). Enhancing Road Safety with Convolutional Neural Network Traffic Sign Classification. Sinkron, 8(4), 2810–2818. https://doi.org/10.33395/sinkron.v8i4.13124
Johnson, G., Argyriou, V., Barman, S., & Politis, C. (2025). Assistive facial expression recognition for children with autism using re-enactment. Computers in Human Behavior Reports, 20(May). https://doi.org/10.1016/j.chbr.2025.100800
Krithika L.B, & Lakshmi Priya GG. (2016). Student Emotion Recognition System (SERS) for e-learning Improvement Based on Learner Concentration Metric. Procedia Computer Science, 85, 767–776. https://doi.org/https://doi.org/10.1016/j.procs.2016.05.264
Li, S., Wang, J., Tian, L., Wang, J., & Huang, Y. (2025). A fine-grained human facial key feature extraction and fusion method for emotion recognition. Scientific Reports, 15(1), 6153. https://doi.org/10.1038/s41598-025-90440-2
Luo, Y., & Huang, L. (2023). Research on the Application of Deep Learning Algorithm in Face Expression Recognition. 2023 Global Conference on Information Technologies and Communications (GCITC), 1–4. https://doi.org/10.1109/GCITC60406.2023.10425903
Manalu, H. V., & Rifai, A. P. (2024). Detection of human emotions through facial expressions using hybrid convolutional neural network-recurrent neural network algorithm. Intelligent Systems with Applications, 21, 200339. https://doi.org/https://doi.org/10.1016/j.iswa.2024.200339
Nan, Y., Ju, J., Hua, Q., Zhang, H., & Wang, B. (2022). A-MobileNet: An approach of facial expression recognition. Alexandria Engineering Journal, 61(6), 4435–4444. https://doi.org/https://doi.org/10.1016/j.aej.2021.09.066
Sajjad, M., Ullah, F. U. M., Ullah, M., Christodoulou, G., Alaya Cheikh, F., Hijji, M., Muhammad, K., & Rodrigues, J. J. P. C. (2023). A comprehensive survey on deep facial expression recognition: challenges, applications, and future guidelines. Alexandria Engineering Journal, 68, 817–840. https://doi.org/https://doi.org/10.1016/j.aej.2023.01.017
Sholikah, R. W., Ginasrdi, R. V. H., Nugroho, S. L. C., Ghozali, K., & Indrawanti, A. S. (2024). Real-time Facial Expression Recognition to Enhance Emotional Intelligence in Autism. Procedia Computer Science, 234, 222–229. https://doi.org/https://doi.org/10.1016/j.procs.2024.02.169
Sun, R., Wang, C., & Wang, Y. (2025). Exploring a non-parametric uncertain adaptive training method for facial expression recognition. Journal of Visual Communication and Image Representation, 104636. https://doi.org/https://doi.org/10.1016/j.jvcir.2025.104636
Tabuenca, B., Uche-Soria, M., Greller, W., Hernández-Leo, D., Balcells-Falgueras, P., Gloor, P., & Garbajosa, J. (2024). Greening smart learning environments with Artificial Intelligence of Things. Internet of Things, 25, 101051. https://doi.org/https://doi.org/10.1016/j.iot.2023.101051
Tonguç, G., & Ozaydın Ozkara, B. (2020). Automatic recognition of student emotions from facial expressions during a lecture. Computers & Education, 148, 103797. https://doi.org/https://doi.org/10.1016/j.compedu.2019.103797
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2026 Sandy Radytia, Ucuk Darusalam

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


Moraref
PKP Index
Indonesia OneSearch
OCLC Worldcat
Index Copernicus
Scilit




















