With the rapid evolution of online education, the demand for personalized learning experiences has grown significantly. This paper introduces an innovative approach to enhance e-learning platforms through the integration of a machine learning-based framework. The proposed system focuses on optimizing the Recommender System, leveraging Recommender Systems techniques to tailor educational content to individual learner profiles. Through........
Keywords- Recommender Systems, E-Learning, Recommender System, Dynamic Adaptation, Real time Learning.
[1]. Imran, H., Belghis-Zadeh, M., Chang, T. W., Kinshuk, & Graf, S. (2016). PLORS: A personalized learning object recommender system.
Vietnam Journal of Computer Science, 3, 3–13. https://doi.org/10.1007/s40595-015-0049-6
[2]. George, G., & Lal, A. M. (2019). Review of ontology-based recommender systems in e-learning. Computers & Education, 142, 103642.
[3]. Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020b). Vision, challenges, roles and research issues of artificial intelligence in
education. Computers & Education: Artificial Intelligence, 1, 100001.
[4]. Raj, N. S., & Renumol, V. G. (2021). A systematic literature review on adaptive content recommenders in personalized learning
environments from 2015 to 2020. Journal of Computers in Education. https://doi.org/10.1007/s40692-021-00199-4
[5]. Zhou, Y., Huang, C., Hu, Q., Zhu, J., & Tang, Y. (2018). Personalized learning full-path recommendation model based on LSTM neural
networks. Information Sciences, 444, 135–152.