An Artificial Intelligence Based Recommendation Model for Personalizing Students' Learning Interest Paths at Universities
Keywords:
Personalized Learning, Artificial Intelligence in Education, Adaptive Educational TechnologyAbstract
This study explores the integration of artificial intelligence (AI) in education, particularly in supporting personalized learning. AI presents new opportunities through adaptive learning platforms, virtual tutors, and intelligent assessment systems that have the potential to revolutionize teaching and learning methods. By conducting in-depth data analysis, AI can identify student performance patterns and provide tailored recommendations, enabling educators to deliver more targeted interventions. Furthermore, personalized learning plays a crucial role in enhancing student motivation and engagement by customizing learning experiences to meet individual needs and learning styles. This study aims to implement personalized learning strategies in educational settings and offers insights into best practices for their integration. It also examines their impact on student engagement and academic achievement. The findings highlight the importance of personalized learning in fostering an inclusive and effective educational environment. By leveraging AI, educators can optimize learning, empower students, and address achievement gaps. This study provides practical recommendations for educators and policymakers to implement AI-based learning strategies effectively.
References
Bernacki, M. L., Greene, M. J., & Lobczowski, N. G. (2021). A Systematic Review of Research on Personalized Learning: Personalized by Whom, to What, How, and for What Purpose(s)? In Educational Psychology Review (Vol. 33, Issue 4, pp. 1675–1715). Springer. https://doi.org/10.1007/s10648-021-09615-8
Intayoad, W., Kamyod, C., & Temdee, P. (2020). Reinforcement Learning Based on Contextual Bandits for Personalized Online Learning Recommendation Systems. Wireless Personal Communications, 115(4), 2917–2932. https://doi.org/10.1007/s11277-020-07199-0
Karaoglan Yilmaz, F. G., & Yilmaz, R. (2020). Student Opinions About Personalized Recommendation and Feedback Based on Learning Analytics. Technology, Knowledge and Learning, 25(4), 753–768. https://doi.org/10.1007/s10758-020-09460-8
Khanal, S. S., Prasad, P. W. C., Alsadoon, A., & Maag, A. (2020). A systematic review: machine learning based recommendation systems for e-learning. Education and Information Technologies, 25(4), 2635–2664. https://doi.org/10.1007/s10639-019-10063-9
Lalitha, T. B., & Sreeja, P. S. (2020). Personalised Self-Directed Learning Recommendation System. Procedia Computer Science, 171, 583–592. https://doi.org/10.1016/j.procs.2020.04.063
Li, H., Li, H., Zhang, S., Zhong, Z., & Cheng, J. (2019). Intelligent learning system based on personalized recommendation technology. Neural Computing and Applications, 31(9), 4455–4462. https://doi.org/10.1007/s00521-018-3510-5
Nabizadeh, A. H., Leal, J. P., Rafsanjani, H. N., & Shah, R. R. (2020). Learning path personalization and recommendation methods: A survey of the state-of-the-art. In Expert Systems with Applications (Vol. 159). Elsevier Ltd. https://doi.org/10.1016/j.eswa.2020.113596
Nguyen, V. A., Nguyen, H. H., Nguyen, D. L., & Le, M. D. (2021). A course recommendation model for students based on learning outcome. Education and Information Technologies, 26(5), 5389–5415. https://doi.org/10.1007/s10639-021-10524-0
Peng, H., Ma, S., & Spector, J. M. (2019). Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6(1). https://doi.org/10.1186/s40561-019-0089-y
Raj, N. S., & Renumol, V. G. (2022). A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020. Journal of Computers in Education, 9(1), 113–148. https://doi.org/10.1007/s40692-021-00199-4
Tang, X., Chen, Y., Li, X., Liu, J., & Ying, Z. (2019). A reinforcement learning approach to personalized learning recommendation systems. British Journal of Mathematical and Statistical Psychology, 72(1), 108–135. https://doi.org/10.1111/bmsp.12144
Urdaneta-Ponte, M. C., Mendez-Zorrilla, A., & Oleagordia-Ruiz, I. (2021). Recommendation systems for education: Systematic review. In Electronics (Switzerland) (Vol. 10, Issue 14). MDPI AG. https://doi.org/10.3390/electronics10141611
Wang, H., & Fu, W. (2021). Personalized Learning Resource Recommendation Method Based on Dynamic Collaborative Filtering. Mobile Networks and Applications, 26(1), 473–487. https://doi.org/10.1007/s11036-020-01673-6
