CFP last date
20 October 2025
Call for Paper
November Edition
IJCA solicits high quality original research papers for the upcoming November edition of the journal. The last date of research paper submission is 20 October 2025

Submit your paper
Know more
The week's pick
Responsive image
Zero Trust Architecture Implementation in Enterprise Networks: Evaluating Effectiveness Against Cyber Threats

Stephen Kofi Dotse Samuel Yao Sebuabe Augustus Obeng Silas Asani Abudu Edna Awisie Pappoe
Random Articles
Reseach Article

VALF: A CLA-based Reinforcement Learning Framework for Vietnamese Language Learning in Dyslexic Children

by Vũ Trần Quang Minh, Sharmila Mathivanan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 29
Year of Publication: 2025
Authors: Vũ Trần Quang Minh, Sharmila Mathivanan
10.5120/ijca2025925496

Vũ Trần Quang Minh, Sharmila Mathivanan . VALF: A CLA-based Reinforcement Learning Framework for Vietnamese Language Learning in Dyslexic Children. International Journal of Computer Applications. 187, 29 ( Aug 2025), 22-30. DOI=10.5120/ijca2025925496

@article{ 10.5120/ijca2025925496,
author = { Vũ Trần Quang Minh, Sharmila Mathivanan },
title = { VALF: A CLA-based Reinforcement Learning Framework for Vietnamese Language Learning in Dyslexic Children },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2025 },
volume = { 187 },
number = { 29 },
month = { Aug },
year = { 2025 },
issn = { 0975-8887 },
pages = { 22-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number29/valf-a-cla-based-reinforcement-learning-framework-for-vietnamese-language-learning-in-dyslexic-children/ },
doi = { 10.5120/ijca2025925496 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-08-20T21:35:10+05:30
%A Vũ Trần Quang Minh
%A Sharmila Mathivanan
%T VALF: A CLA-based Reinforcement Learning Framework for Vietnamese Language Learning in Dyslexic Children
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 29
%P 22-30
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Dyslexic children faces many persistent challenges in learning Tonal languages such as Vietnamese, Chinese, Koeran where visual, phonetic, and orthographic components interact in complex ways. This paper introduces a new approach VALF (Vietnamese Adaptive Learning Framework), a novel educational AI prototype that uses Cellular Learning Automata (CLA) to generate adaptive multimedia content for Vietnamese language instruction. VALF integrates a reinforcement learning-driven virtual pen for character rendering, tone marker placement, and audio pronunciation to enhance learners' visual, auditory, and motor integration. Designed for primary school children with dyslexia, the system simulates personalized learning through games, quizzes, and pronunciation feedback. The proposed VALF model can also able to produce long texts and sentences using Vietnamese Word Generation algorithm. Main objectives of VALF’s efficacy is to promote literacy and self-confidence in dyslexic learners. Future work includes implementing VALF in gaming platform to support and integrate deep learning for natural speech synthesis and feedback.

References
  1. Minoofam, S. A. H., Bastanfard, A., & Keyvanpour, M. R. (2022). RALF: An adaptive reinforcement learning framework for teaching dyslexic students. Multimedia Tools and Applications, 81(5), 6389–6412.
  2. Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019). Reinforcement learning, fast and slow. Trends in Cognitive Sciences, 23(5), 408–422.
  3. Carlotto, T., & Jaques, P. A. (2016). The effects of animated pedagogical agents in an English-as-a-Foreign-Language learning environment. International Journal of Human-Computer Studies, 95, 15–26.
  4. Rello, L., & Baeza-Yates, R. (2016). The effect of font type on screen readability by people with dyslexia. ACM Transactions on Accessible Computing (TACCESS), 8(4), 1–33.
  5. Rubio, G., Navarro, E., & Montero, F. (2014). APADYT: a multimedia application for SEN learners. Multimedia Tools and Applications, 71(3), 1771–1802.
  6. Peng, X., Chen, H., Wang, L., & Wang, H. (2018). Evaluating a 3-D virtual talking head on pronunciation learning. International Journal of Human-Computer Studies, 109, 26–40.
  7. Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3–4), 229–256.
  8. Watkins, C. J. C. H. (1989). Learning from Delayed Rewards (PhD thesis).
  9. Watkins, C. J. C. H., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3-4), 279–292.
  10. Riedmiller, M. (2005). Neural fitted Q iteration–first experiences with a data efficient neural reinforcement learning method. In European Conference on Machine Learning (pp. 317–328).
  11. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.
  12. Lin, L.-J. (1992). Self-improving reactive agents based on reinforcement learning, planning and teaching. Machine Learning, 8(3–4), 293–321.
  13. Van Hasselt, H., Guez, A., & Silver, D. (2016). Deep reinforcement learning with double Q-learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 30, No. 1).
  14. Wang, Z., Schaul, T., Hessel, M., van Hasselt, H., et al. (2016). Dueling network architectures for deep reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning (pp. 1995–2003).
  15. Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2015). Prioritized experience replay. arXiv preprint arXiv:1511.05952.
  16. Konda, V. R., & Tsitsiklis, J. N. (2000). Actor-Critic algorithms. In Advances in Neural Information Processing Systems (pp. 1008–1014).
  17. Mnih, V., Badia, A. P., Mirza, M., Graves, A., et al. (2016). Asynchronous methods for deep reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning (pp. 1928–1937).
  18. Lillicrap, T. P., Hunt, J. J., Pritzel, A., et al. (2015). Continuous control with deep reinforcement learning.
  19. Haarnoja, T., Zhou, A., Abbeel, P., & Levine, S. (2018). Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In Proceedings of the 35th International Conference on Machine Learning (pp. 1861–1870).
  20. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms.
  21. Schulman, J., Levine, S., Abbeel, P., Jordan, M., & Moritz, P. (2015). Trust region policy optimization. In International Conference on Machine Learning (pp. 1889–1897).
  22. OpenAI (2019). OpenAI Five. https://openai.com/blog/openai-five/.
  23. Raffin, A., Hill, A., Gleave, A., Kanervisto, A., & Ernestus, M. (2021). Stable-Baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research, 22(268), 1–8.
  24. Lillicrap, T. P., Hunt, J. J., Pritzel, A., et al. (2015). Continuous control with deep reinforcement learning.
  25. Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., & Riedmiller, M. (2014). Deterministic policy gradient algorithms. In Proceedings of the 31st International Conference on Machine Learning (pp. 387–395).
  26. Fujimoto, S., Van Hoof, H., & Meger, D. (2018). Addressing function approximation error in actor-critic methods. In International Conference on Machine Learning (pp. 1587–1596).
  27. Haarnoja, T., Zhou, A., Abbeel, P., & Levine, S. (2018). Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In Proceedings of the 35th International Conference on Machine Learning (pp. 1861–1870).
  28. Duong, T. H. D., & Vo, T. T. H. (2024). Fostering Vietnamese EFL learners' learner autonomy and reading comprehension ability through online platforms. European Journal of English Language Teaching, 9(4), 113–131.
  29. Smith, C., & Hattingh, M. J. (2020). Assistive technologies for students with dyslexia: A systematic literature review. In International Conference on Innovative Technologies and Learning, Springer, pp. 504–513.
  30. Taskov, T., & Dushanova, J. (2021). Small-World Propensity in Developmental Dyslexia After Visual Training Intervention. In Intelligent Computing. Springer, pp. 233–258.
  31. D. Lam, Vietnamese Typography. [Online]. Available: https://vietnamesetypography.com/.
  32. S. A. H. Minoofam, A. Bastanfard, and M. R. Keyvanpour, “RALF: An adaptive reinforcement learning framework for teaching dyslexic students,” Multimedia Tools and Applications, vol. 81, no. 5, pp. 6389–6412, 2022. doi: 10.1007/s11042-021-11806-y.
  33. International Dyslexia Association, “Understanding dyslexia and the reading brain in kids,” 2020. [Online]. Available: https://dyslexiaida.org.
  34. LDAU, “Multisensory learning and its effects on dyslexia,” 2021. [Online]. Available: https://ldau.org
  35. G. Rubio, E. Navarro, and F. Montero, “APADYT: A multimedia application for SEN learners,” Multimedia Tools and Applications, vol. 71, no. 3, pp. 1771–1802, 2014. doi: 10.1007/s11042-012-1304-9.
  36. Nord Anglia Education, “Vietnamese National Curriculum,” [Online]. Available: https://www.nordangliaeducation.com/.
  37. S. A. H. Minoofam, A. Bastanfard, and M. R. Keyvanpour, “RALF: An adaptive reinforcement learning framework for teaching dyslexic students,” Multimedia Tools and Applications, vol. 81, no. 5, pp. 6389–6412, 2022. doi: 10.1007/s11042-021-11806-y.
  38. A. Ahmad and M. S. Ahmed, “An Interactive Learning Platform for Dyslexic Children using Multimedia and Game-based Techniques,” International Journal of Interactive Mobile Technologies, vol. 14, no. 6, pp. 127–139, 2020. [Online]. Available: https://www.warse.org.
  39. CAST, “Audio-Supported Reading for Students with Learning Disabilities,” CAST Research Reports, 2021. [Online]. Available: https://aem.cast.org.
  40. A. Smith and C. Hattingh, “Assistive technologies for students with learning disabilities: A review,” Disability and Rehabilitation: Assistive Technology, vol. 15, no. 1, pp. 11–24, 2020. doi: 10.3109/17483107.2019.1573840.
  41. H. Yildirim and E. Surer, “Design and Evaluation of Adaptive Serious Games for Dyslexic Children,” PubMed Central, 2021. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov.
  42. A. K. Singh and R. D. Rishi, “An Adaptive E-Learning Framework for Children with Dyslexia,” PubMed Central, 2020. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc.
  43. A. T. Albano, “The Future of Adaptive Testing,” Credentialing Insights, vol. 31, no. 2, pp. 10–15, 2023. [Online]. Available: https://www.credentialinginsights.org.
  44. Acapela Group, “Text-to-speech solutions for dyslexia,” Acapela Group, 2022. [Online]. Available: https://www.acapela-group.com.
Index Terms

Computer Science
Information Sciences

Keywords

Vietnamese language learning Educational AI Reinforcement learning Cellular Learning Automata (CLA) Dyslexia Computer-assisted language learning (CALL) Adaptive learning systems Child education technology Multimodal learning Tone-based script modelling.

Powered by PhDFocusTM