Identifying the Learning Style of Students Using Reinforcement Learning Techniques An Approach of Felder-Silverman Learning Style Model (FSLSM)
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Jan 25, 2026
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Abstract
Understanding how students learn is key to making education more effective. This research presents an innovative, automated method for identifying students’ learning styles using artificial intelligence. This research employed the Felder–Silverman Learning Style Model (FSLSM), which examines how students process information, prefer input (visual or verbal), and comprehend concepts. Instead of asking students to fill out long forms every time, this study trained a Q-Learning agent, a type of reinforcement learning, to recognize learning patterns directly from questionnaire data. This study tested this approach using data from 799 students from various universities in Indonesia. The results showed that the model could accurately predict learning styles in almost every case, particularly in how students process and understand information. In a follow-up test with 50 students, the model achieved 100% accuracy, matching traditional FSLSM assessments perfectly. This demonstrates that Q-Learning can be a powerful tool for automatically identifying learning styles. It opens up new possibilities for creating personalized and adaptive learning systems that adjust materials and methods based on each student’s unique style. Moving forward, the system can be improved to better handle cases where certain learning styles are underrepresented
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[1]
N. SYAFITRI, S. Mohamad Samuri, and Y. Arta, “Identifying the Learning Style of Students Using Reinforcement Learning Techniques”, INFOTEL, vol. 17, no. 4, pp. 872-891, Jan. 2026.
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Informatics
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[2] F. Demir, C. Bruce-Kotey, and F. Alenezi, “User Experience Matters: Does One size Fit all? Evaluation of Learning Management Systems,” Technol. Knowl. Learn., vol. 27, no. 1, pp. 49–67, Mar. 2022, doi: 10.1007/s10758-021-09518-1.
[3] R. M. Felder and L. K. Silverman, “Learning and Teaching Styles in Engineering Education,” 1988. [Online]. Available: https://api.semanticscholar.org/CorpusID:140475379
[4] A. Hidayat, K. Adi, and B. Surarso, “Detection of Student Learning Styles Using the Index of Learning Style,” 2021.
[5] J. Bernard, T.-W. Chang, E. Popescu, and S. Graf, “Learning style Identifier: Improving the precision of learning style identification through computational intelligence algorithms,” Expert Syst. Appl., vol. 75, pp. 94–108, Jun. 2017, doi: 10.1016/j.eswa.2017.01.021.
[6] M. S. Hasibuan, R. A. Aziz, D. A. Dewi, T. B. Kurniawan, and N. A. Syafira, “Recommendation Model for Learning Material Using the Felder Silverman Learning Style Approach,” HighTech Innov. J., vol. 4, no. 4, pp. 811–820, Dec. 2023, doi: 10.28991/HIJ-2023-04-04-010.
[7] C. S. Claxton and P. H. Murrell, Learning styles : implications for improving educational practices. Association for the Study of Higher Education(US)(ASHE), 1987.
[8] W. WAAM and P. HKS, “Identifying the Learning Style of Students Using Machine Learning Techniques: An Approach of Felder Silverman Learning Style Model (FSLSM),” Asian J. Res. Comput. Sci., vol. 17, no. 3, pp. 15–37, Jan. 2024, doi: 10.9734/ajrcos/2024/v17i3422.
[9] M. S. Hasibuan and O. W. Purbo, “Learning Motivation increased due to a Relaxed Assessment in a Competitive-Learning Environment,” Proceeding Electr. Eng. Comput. Sci. Informatics, vol. 5, no. 5, Nov. 2018, doi: 10.11591/eecsi.v5i5.1616.
[10] A. M. Altamimi, M. Azzeh, and M. Albashayreh, “Predicting students’ learning styles using regression techniques,” Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 2, pp. 1177–1185, Feb. 2022, doi: 10.11591/ijeecs.v25.i2.pp1177-1185.
[11] N. D. Fleming and C. Mills, “Not Another Inventory, Rather a Catalyst for Reflection,” To Improv. Acad., vol. 11, no. 1, pp. 137–155, Jun. 1992, doi: 10.1002/j.2334-4822.1992.tb00213.x.
[12] D. A. Kolb, “Experiential Learning: Experience as the Source of Learning and Development,” 1983. [Online]. Available: https://api.semanticscholar.org/CorpusID:146755699
[13] R. M. Felder, “Learning and Teaching Styles in Engineering Education,” 1988. [Online]. Available: http://www.ncsu.edu/felder-public/ILSpage.html
[14] P. Honey and A. Mumford, Style of Learning. Gower Handb. Manag. Dev., 1994.
[15] M. S. Hasibuan and R. A. Aziz, “Systematic Literature Review Detection Learning Style,” in 2022 International Conference on Platform Technology and Service (PlatCon), 2022, pp. 29–33. doi: 10.1109/PlatCon55845.2022.9932087.
[16] M. Jebbari, B. Cherradi, S. Hamida, and A. Raihani, “Identifying learning styles in MOOCs environment through machine learning predictive modeling,” Educ. Inf. Technol., Apr. 2024, doi: 10.1007/s10639-024-12637-8.
[17] R. R. Maaliw, “Classification of Learning Styles in Virtual Learning Environment using Data Mining: A Basis for Adaptive Course Design,” Jul. 2016.
[18] C. Waladi, M. Khaldi, and M. Lamarti Sefian, “Machine Learning Approach for an Adaptive E-Learning System Based on Kolb Learning Styles,” Int. J. Emerg. Technol. Learn., vol. 18, no. 12, pp. 4–15, 2023, doi: 10.3991/ijet.v18i12.39327.
[19] S. Sharuni, R. Dhana Lakshmi, and A. Murugappan, “Automatic Detection of Learner’s Learning Style,” 2024, pp. 77–87. doi: 10.1007/978-981-97-0037-0_6.
[20] I. Azzi, A. Radouane, L. Laaouina, A. Jeghal, A. Yahyaouy, and H. Tairi, “Fuzzy Classification Approach to Select Learning Objects Based on Learning Styles in Intelligent E-Learning Systems,” Informatics, vol. 11, no. 2, p. 29, May 2024, doi: 10.3390/informatics11020029.
[21] O. El Aissaoui, Y. E. M. El Alami, L. Oughdir, and Y. El Allioui, “A Hybrid Machine Learning Approach to Predict Learning Styles in Adaptive E-Learning System,” in Advanced Intelligent Systems for Sustainable Development (AI2SD’2018), 2019, pp. 772–786.
[22] B. Muhammad et al., “A Fuzzy C-means Algorithm to Detect Learning Styles in Online Learning Environment,” J. Netw. Netw. Appl., vol. 4, p. 39, Jun. 2024, doi: 10.33969/J-NaNA.2024.040105.
[23] A. Ezzaim, A. Dahbi, A. Aqqal, and A. Haidine, “AI-based learning style detection in adaptive learning systems: a systematic literature review,” J. Comput. Educ., Jun. 2024, doi: 10.1007/s40692-024-00328-9.
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[25] M. Boussakssou, B. Hssina, and M. Erittali, “Towards an Adaptive E-learning System Based on Q-Learning Algorithm,” Procedia Comput. Sci., vol. 170, pp. 1198–1203, 2020, doi: 10.1016/j.procs.2020.03.028.
[26] K. Fahd, S. Venkatraman, S. J. Miah, and K. Ahmed, “Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature,” Educ. Inf. Technol., vol. 27, no. 3, pp. 3743–3775, Apr. 2022, doi: 10.1007/s10639-021-10741-7.
[27] Z. Mehenaoui, Y. Lafifi, and L. Zemmouri, “Learning Behavior Analysis to Identify Learner’s Learning Style based on Machine Learning Techniques,” JUCS - J. Univers. Comput. Sci., vol. 28, no. 11, pp. 1193–1220, Nov. 2022, doi: 10.3897/jucs.81518.
[28] J. L. Fleiss and J. Cohen, “The Equivalence of Weighted Kappa and the Intraclass Correlation Coefficient as Measures of Reliability,” Educ. Psychol. Meas., vol. 33, no. 3, pp. 613–619, Oct. 1973, doi: 10.1177/001316447303300309