ETS-TDSVM for Personalized French Learning: An Advanced Framework for Vietnamese Police Cadets
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Abstract
In the context of globalized policing and international co-operation, the ability to communicate effectively in foreign languages has become increasingly essential for law enforcement officers. This research presents a machine learning (ML)-based frameworks to overcome the limitations of static models and provide real-time, personalized French language instruction at the People's Police Academy of Vietnam. The system leverages learner-specific data to dynamically adapt instructional content and progression, thereby enhancing language acquisition efficiency in a professional training environment. Data was collected over 16 weeks from 378 police cadets in Vietnam using a blended learning platform. Sources included weekly digital assessments, pronunciation accuracy metrics, user interaction logs, response times, and in-app feedback. The framework integrates the K-means clustering algorithm that initially segments learners based on behavioral and performance patterns. An Enriched Transient Search Tree-tuned Dynamic Support Vector Machine (ETS-TDSVM) model is applied to classify language proficiency levels, enabling adaptive content delivery. Implemented in Python, the ETS-TDSVM classifier achieved a high accuracy of 93.5% in proficiency level prediction. The integration of adaptive parameter optimization through the Enriched Transient Search Tree significantly enhances the convergence speed and predictive reliability of the Dynamic Support Vector Machine. Experimental evaluation demonstrates improved learner clustering consistency and reduced misclassification rates compared with baseline models. The results indicate that data-driven adaptive learning systems can substantially improve personalized instruction and learner engagement in specialized professional education environments. The high accuracy of the proposed framework demonstrates the potential to enhance language acquisition efficiency in specialized professional training institutions globally.
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References
Cai, J. and Li, Y., (2024). Fuzzy association rule mining for Personalized English Language Teaching in higher education. Journal of Computational Methods in Sciences and Engineering, 24(6), 3617-3631.https://doi.org/10.1177/14727978241296748
Chaipidech, P., Srisawasdi, N., Kajornmanee, T., and Chaipah, K. (2022). A personalized learning system-supported professional training model for teachers' TPACK development. Computers and Education: Artificial Intelligence, 3,100064.https://doi.org/10.1016/j.caeai.2022.100064
Chen, J., Liu, Z., Huang, X., Wu, C., Liu, Q., Jiang, G., Pu, Y., Lei, Y., Chen, X., Wang, X. and Zheng, K., (2024). When large language models meet personalization: Perspectives of challenges and opportunities. World Wide Web, 27(4), .42.https://doi.org/10.1007/s11280-024-01276-1
Chen, Y., (2024), May. Enhancing language acquisition: The role of AI in facilitating effective language learning. In 2024, the 3rd International Conference on Humanities, Wisdom Education and Service Management (HWESM 2024) (593-600). Atlantis Press https://doi.org/10.2991/978-2-38476-253-8_71
Dong, W., Pan, D., and Kim, S. (2024). Exploring the integration of IoT and Generative AI in English language education: Smart tools for personalized learning experiences. Journal of Computational Science, 82, 102397.https://doi.org/10.1016/j.jocs.2024.102397
Ezhilmathi, K., Durairaj, M., Vazhangal, M., Mohammad, R., Pathak, P., and Karthik, M. (2024), July. Tailored English Language Learning Support: Leveraging Long Short-Term Memory Networks for Personalized Assistance. In 2024, the Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), 1-6. IEEE.https://doi.org/10.1109/ICEEICT61591.2024.10718506
Gan, W., Qi, Z., Wu, J., and Lin, J.C.W. (2023), December. Large language models in education: Vision and opportunities. In 2023 IEEE international conference on big data (BigData),4776-4785. IEEE.https://doi.org/10.1109/BigData59044.2023.10386291
Hussain, S.M., Satti, S.M.J., and Khan, Z. (2024). AI-Powered Personalized Learning: Advancing Language Education in the Digital Era. Journal of Social Signs Review, 2(4), 730-740.
Istanti, W., Pratiwi, S., and Saddhono, K. (2024), November. AI-Driven Personalized Learning: Revolutionizing Language Education. In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT),329-334. IEEE.10.1109/ICICAT62666.2024.10923274
Kanchon, M.K.H., Sadman, M., Nabila, K.F., Tarannum, R., and Khan, R. (2024). Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies. International Journal of Cognitive Computing in Engineering, 5,269-278. https://doi.org/10.1016/j.ijcce.2024.06.002
Liu, C. and Yang, S., (2024). Personalized learning ability classification using SVM for enhanced education in system modeling and simulation courses. Frontiers of Digital Education, 1(4), 295-307. https://doi.org/10.1007/s44366-024-0035-6
Naseer, F., Khan, M.N., Tahir, M., Addas, A., and Aejaz, S.H. (2024). Integrating deep learning techniques for personalized learning pathways in higher education. Heliyon, 10(11).
Orosoo, M., Raash, N., Treve, M., Lahza, H.F.M., Alshammry, N., Ramesh, J.V.N., and Rengarajan, M. (2025). Transforming English language learning: Advanced speech recognition with MLP-LSTM for personalized education. Alexandria Engineering Journal, 111,21-32.https://doi.org/10.1016/j.aej.2024.10.065
Peng, X. and Wang, Y. (2025). An AI-Driven Approach for Advancing English Learning in Educational Information Systems Using Machine Learning. International Journal of Advanced Computer Science & Applications, 16(1).https://doi.org/10.14569/ijacsa.2025.0160116
Praveena, T. and Anupama, K. (2025). Machine Learning Meets Language Learning: The Transformative Potential of Artificial Intelligence in English Language Instruction. Human Research in Rehabilitation, 15(1).https://doi.org/10.21554/hrr.04251
Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., and Demir, I. (2024). Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. Information, 15(10), 596.https://doi.org/10.3390/info15100596
Song, C., Shin, S.Y., and Shin, K.S. (2024). Implementing the dynamic feedback-driven learning optimization framework: a machine learning approach to personalize educational pathways. Applied Sciences, 14(2), 916. https://doi.org/10.3390/app14020916
Sun, B. (2025). Gated recurrent deep learning approaches to revolutionizing English language learning for personalized instruction and effective instruction. Scientific Reports, 15(1), 13028. https://doi.org/10.1038/s41598-025-96351-6
Sun, Y., (2025). Construction and optimization of personalized learning paths for English learners based on the SSA-LSTM model. Systems and Soft Computing, 7, 200218.https://doi.org/10.1016/j.sasc.2025.200218
Tanweer, M. and Ismail, A. (2024). Generative AI in curriculum development: A framework for adaptive, customized, and personalized learning. In Impacts of generative AI on creativity in higher education,193-226. IGI Global. https://doi.org/10.4018/979-8-3693-2418-9.ch008
Wei, L. (2023). Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning. Frontiers in psychology, 14, 1261955.https://doi.org/10.3389/fpsyg.2023.1261955
Xia, Y., Shin, S.Y. and Shin, K.S., (2024). Designing personalized learning paths for foreign language acquisition using big data: Theoretical and empirical analysis. Applied Sciences, 14(20), 9506. https://doi.org/10.3390/app14209506