Integrating affective computing and deep learning for learning path optimization in vocational education

HongLi Zhang; Wai Yie Leong; Yan Li

doi:10.29333/ejmste/17671

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HongLi Zhang ¹ ² , Wai Yie Leong ² ^* , Yan Li ¹ ²

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¹ Heilongjiang Institute of Construction Technology, Harbin, CHINA² Faculty of Engineering and Quantity Surveying, INTI International University, Negeri Sembilan, MALAYSIA^* Corresponding Author

Abstract

This study addresses the challenges of traditional vocational education systems, which often fail to meet learners’ diverse needs in dynamic and emotionally complex environments. To bridge this gap, we propose an intelligent learning system that integrates multimodal affective computing with deep Q-network algorithms for personalized learning path optimization. By leveraging multimodal data fusion, the system enhances the accuracy of emotion recognition and dynamically adjusts learning paths in real time. Experimental results show a 27% improvement in learning efficiency, an 86% accuracy rate in personalized recommendations, and an 8% increase in student performance compared to conventional methods. Furthermore, a privacy-preserving architecture utilizing federated learning ensures secure large-scale applications. This study highlights the transformative potential of integrating affective computing and reinforcement learning in vocational education and sets the stage for broader applications in personalized learning systems.

Keywords

deep Q-network
multimodal data fusion
emotion recognition
personalized learning
vocational education

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, Volume 22, Issue 1, January 2026, Article No: em2760

https://doi.org/10.29333/ejmste/17671

Publication date: 01 Jan 2026

Online publication date: 31 Dec 2025

Article Views: 58

Article Downloads: 22

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References

Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., & Jararweh, Y. (2022). Federated learning review: Fundamentals, enabling technologies, and future applications. Information Processing & Management, 59(6), Article 103061. https://doi.org/10.1016/j.ipm.2022.103061
Chen, Y., Li, D., Zhang, P., Sui, J., Lv, Q., Tun, L., & Shnag, L. (2022). Cross-modal ambiguity learning for multimodal fake news detection. In Proceedings of the ACM Web Conference 2022 (pp. 2897-2905). ACM. https://doi.org/10.1145/3485447.3511968
Ekman, P., & Friesen, W. V. (1971). Constants across cultures in the face and emotion. Journal of Personality and Social Psychology, 17(2), 124-129. https://doi.org/10.1037/h0030377
Fan, J., Wang, Z., Xie, Y., & Yang, Z. (2020). A theoretical analysis of deep Q-learning. In Proceedings of the 2^nd Conference on Learning for Dynamics and Control (pp. 486-489).
Fang, A., Zhao, X., & Zhang, Y. (2020). Cross-modal image fusion guided by subjective visual attention. Neurocomputing, 414, 333-345. https://doi.org/10.1016/j.neucom.2020.07.014
Gu, S., Yang, L., Du, Y., Chen, G., Walter, F., Wang, J., & Knoll, A. (2022). A review of safe reinforcement learning: Methods, theory and applications. arXiv. https://doi.org/10.48550/arXiv.2205.10330
Guo, S., Zhang, X., Du, Y., Zheng, Y., & Cao, Z. (2021). Path planning of coastal ships based on optimized DQN reward function. Journal of Marine Science and Engineering, 9(2), Article 210. https://doi.org/10.3390/jmse9020210
Järvelä, S., Gašević, D., Seppänen, T., Pechenizkiy, M., & Kirschner, P. A. (2020). Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning. British Journal of Educational Technology, 51(6), 2391-2406. https://doi.org/10.1111/bjet.12917
Leong, W. Y. (2025a). AI-driven optical illusions: Innovations in perceptual art and design. Innovation on Design and Culture, 4(1), 1-14.
Leong, W. Y. (2025b). Machine learning in evolving art styles: A study of algorithmic creativity. Engineering Proceedings, 92(1), Article 45. https://doi.org/10.3390/engproc2025092045
Li, J., Chen, Y., Zhao, X. N., & Huang, J. (2022). An improved DQN path planning algorithm. The Journal of Supercomputing, 78(1), 616-639. https://doi.org/10.1007/s11227-021-03878-2
Li, S. E. (2023). Reinforcement learning for sequential decision and optimal control. Springer. https://doi.org/10.1007/978-981-19-7784-8
Mammen, P. M. (2021). Federated learning: Opportunities and challenges. arXiv. https://doi.org/10.48550/arXiv.2101.05428
Marín-Morales, J., Llinares, C., Guixeres, J., & Alcañiz, M. (2020). Emotion recognition in immersive virtual reality: From statistics to affective computing. Sensors, 20(18), Article 5163. https://doi.org/10.3390/s20185163
Matsuo, Y., LeCun, Y., Sahani, M., Precup, D., Silver, D., Sugiyama, M., Uchibe, E., & Morimoto, J. (2022). Deep learning, reinforcement learning, and world models. Neural Networks, 152, 267-275. https://doi.org/10.1016/j.neunet.2022.03.037
Mocanu, B., Tapu, R., & Zaharia, T. (2023). Multimodal emotion recognition using cross modal audio-video fusion with attention and deep metric learning. Image and Vision Computing, 133, Article 104676. https://doi.org/10.1016/j.imavis.2023.104676
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Poor, H. V. (2021). Federated learning for internet of things: A comprehensive survey. IEEE Communications Surveys & Tutorials, 23(3), 1622-1658. https://doi.org/10.1109/COMST.2021.3075439
Picard, R. W. (2000). Affective computing. MIT Press. https://doi.org/10.1007/978-3-540-45012-2_2
Rahate, A., Walambe, R., Ramanna, S., & Kotecha, K. (2022). Multimodal co-learning: Challenges, applications with datasets, recent advances and future directions. Information Fusion, 81, 203-239. https://doi.org/10.1016/j.inffus.2021.12.003
Shoumy, N. J., Ang, L.-M., Seng, K. P., Rahaman, M., & Zia, T. (2020). Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals. Journal of Network and Computer Applications, 149(3), Article 102447. https://doi.org/10.1016/j.jnca.2019.102447
Szepesvári, C. (2022). Algorithms for reinforcement learning. Springer.
Tian, Y., Kanade, T., & Cohn, J. F. (2001). Recognizing action units for facial expression analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2), 97-115. https://doi.org/10.1109/34.908962
Wang, X., Wang, S., Liang, X., Zhao, D., Huang, J., Xu, X., Dai, B., & Miao, Q. (2024). Deep reinforcement learning: A survey. IEEE Transactions on Neural Networks and Learning Systems, 35(4), 5064-5078. https://doi.org/10.1109/TNNLS.2022.3207346
Wu, Y.-C., Dinh, T. Q., Fu, Y., Lin, C., & Quek, T. Q. S. (2021). A hybrid DQN and optimization approach for strategy and resource allocation in MEC networks. IEEE Transactions on Wireless Communications, 20(7), 4282-4295. https://doi.org/10.1109/TWC.2021.3057882
Xu, X., Wang, T., Yang, Y., & Zuo, L. (2020). Cross-modal attention with semantic consistence for image-text matching. IEEE Transactions on Neural Networks and Learning Systems, 31(12), 5412-5425. https://doi.org/10.1109/TNNLS.2020.2967597
Yang, Y., Juntao, L., & Lingling, P. (2020). Multi-robot path planning based on a deep reinforcement learning DQN algorithm. CAAI Transactions on Intelligence Technology, 5(3), 177-183. https://doi.org/10.1049/trit.2020.0024
Yu, Z., Li, X., & Zhao, G. (2021). Facial-video-based physiological signal measurement: Recent advances and affective applications. IEEE Signal Processing Magazine, 38(6), 50-58. https://doi.org/10.1109/MSP.2021.3106285
Zhang, H. L., & Leong, W. Y. (2024a). AI solutions for accessible education in underserved communities. Journal of Innovation and Technology, 2024(11). https://doi.org/10.61453/joit.v2024no11
Zhang, H. L., & Leong, W. Y. (2024b). Emotion-driven AI collaboration and multimodal learning in vocational education. In Proceedings of the 2024 International Conference on Intelligent Education and Intelligent Research (pp. 1-6). https://doi.org/10.1109/IEIR62538.2024.10959747
Zhang, H. L., & Leong, W. Y. (2024c). Industry 5.0 and Education 5.0: Transforming vocational education through intelligent technology. Journal of Innovation and Technology, 2024(16). https://doi.org/10.61453/joit.v2024no16
Zhang, H. L., & Leong, W. Y. (2024d). Integration of image retrieval and machine learning in smart manufacturing. In Proceedings of the 2024 International Conference on Informatics, Multimedia, Cyber and Information System (pp. 519-524). IEEE. https://doi.org/10.1109/ICIMCIS63449.2024.10957602
Zhang, H. L., & Leong, W. Y. (2024e). Transforming rural and underserved schools with AI-powered education solutions. ASM Science Journal, 19. https://doi.org/10.32802/asmscj.2023.1895
Zhang, H. L., & Leong, W. Y. (2025a). Industry 5.0 and personalised education: Application of intelligent technology in the optimisation of vocational education resources and improvement of teaching methods. In W. Y. Leong (Ed.), Industry 5.0: Design, standards, techniques and applications for manufacturing (pp. 373-414). Institution of Engineering & Technology. https://doi.org/10.1049/PBME026E_ch18
Zhang, H. L., & Leong, W. Y. (2025b). The personalised emotion-driven AI model for elderly care: Optimising emotion recognition and adaptive response mechanisms. ASM Scientific Journal, 20(1). https://doi.org/10.32802/asmscj.2025.1980
Zhao, F., Zhang, C., & Geng, B. (2024). Deep multimodal data fusion. ACM Computing Surveys, 56(9), 1-36. https://doi.org/10.1145/3649447
Zhou, X., Yang, Q., Zheng, X., Liang, W., Wang, K. I.-K., Ma, J., Pan, Y., & Jin, Q. (2024). Personalized federation learning with model-contrastive learning for multi-modal user modeling in human-centric metaverse. IEEE Journal on Selected Areas in Communications, 42(4), 817-831. https://doi.org/10.1109/JSAC.2023.3345431
Zhu, H., Xu, J., Liu, S., & Jin, Y. (2021). Federated learning on non-IID data: A survey. Neurocomputing, 465, 371-390. https://doi.org/10.1016/j.neucom.2021.07.098

How to cite this article

APA

Zhang, H., Leong, W. Y., & Li, Y. (2026). Integrating affective computing and deep learning for learning path optimization in vocational education. Eurasia Journal of Mathematics, Science and Technology Education, 22(1), em2760. https://doi.org/10.29333/ejmste/17671

Vancouver

Zhang H, Leong WY, Li Y. Integrating affective computing and deep learning for learning path optimization in vocational education. EURASIA J Math Sci Tech Ed. 2026;22(1):em2760. https://doi.org/10.29333/ejmste/17671

AMA

Zhang H, Leong WY, Li Y. Integrating affective computing and deep learning for learning path optimization in vocational education. EURASIA J Math Sci Tech Ed. 2026;22(1), em2760. https://doi.org/10.29333/ejmste/17671

Chicago

Zhang, HongLi, Wai Yie Leong, and Yan Li. "Integrating affective computing and deep learning for learning path optimization in vocational education". Eurasia Journal of Mathematics, Science and Technology Education 2026 22 no. 1 (2026): em2760. https://doi.org/10.29333/ejmste/17671

Harvard

Zhang, H., Leong, W. Y., and Li, Y. (2026). Integrating affective computing and deep learning for learning path optimization in vocational education. Eurasia Journal of Mathematics, Science and Technology Education, 22(1), em2760. https://doi.org/10.29333/ejmste/17671

MLA

Zhang, HongLi et al. "Integrating affective computing and deep learning for learning path optimization in vocational education". Eurasia Journal of Mathematics, Science and Technology Education, vol. 22, no. 1, 2026, em2760. https://doi.org/10.29333/ejmste/17671