A Study on Machine Learning Techniques for Enhancing Online Education Systems
Author
Om Rawat, Shivam Rai, Shashank Kumar, Shivank Shukla, Subhash Pathak, Ravi Vaswani
Abstract
The rapid growth of online education has created a demand for intelligent and adaptive learning systems. Machine Learning (ML), a subset of Artificial Intelligence (AI), provides powerful tools to analyze educational data, predict student performance, and personalize learning experiences. This paper explores the application of ML techniques in addressing key challenges in online education systems, including personalization, engagement, dropout prediction, and automated assessment. The study highlights system architecture, methodologies, challenges, and future research directions. Results indicate that ML significantly enhances the effectiveness and scalability of online learning platforms.
Keywords
Machine Learning, Online Education, Learning Management Systems, Predictive Analytics, Artificial Intelligence, Personalized Learning
Full Text:
References
[1] C. Peng et al., “Artificial Intelligence and Machine Learning for Online Education,” IEEE Access, vol. 10, pp. 12345–12358, 2022. [2] N. Upadhyay and A. Jain, “Integration of Machine Learning in Learning Management Systems,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 3, pp. 45–52, 2023. [3] R. Shafique et al., “Role of Artificial Intelligence in Online Education: A Systematic Mapping Study,” Education and Information Technologies, vol. 28, pp. 567–589, 2023. [4] S. C. Hoi et al., “Online Learning: A Comprehensive Survey,” Neurocomputing, vol. 459, pp. 249–289, 2021. [5] H. Li et al., “Enhancing Student Engagement Using AI-Based Learning Systems,” Education Sciences, vol. 14, no. 3, pp. 389–402, 2024. [6] H. Li et al., “A Machine Learning Approach for Student Dropout Prediction,” arXiv preprint arXiv:2003.09670, 2020.