Authors : Faisal AlDhahi

Volume/Issue : Volume 9 - 2024, Issue 12 - December

Google Scholar : https://tinyurl.com/4e5exjjs

Scribd : https://tinyurl.com/t9rr8w7e

DOI : https://doi.org/10.5281/zenodo.14539896

Abstract : The advent of machine learning (ML) has revolutionized various industries, with education being a pivotal area of transformation. Personalized education systems, which adapt to the unique needs and learning styles of students, have gained significant traction in recent years. This study investigates the integration of machine learning in personalized education systems, focusing on its impact on student outcomes, engagement, and accessibility. Using a combination of literature review and data analysis, the research explores the potential benefits and challenges of implementing ML algorithms in adaptive learning platforms (Lu Wang, 2022; Lei Ma & Jian Li, 2022). The findings highlight increased efficiency in curriculum delivery, improved student retention rates, and enhanced adaptability for diverse learners (Zhai, 2021). However, ethical considerations and data privacy remain critical concerns (Allogmany & Josyula, 2022). This paper provides recommendations for leveraging ML effectively while addressing potential limitations, contributing to the broader discourse on future-ready education systems.

References :

1. Allogmany, B., & Josyula, D. (2022). An approach to dealing with incremental concept drift in personalized learning systems. Proceedings of the IEEE International Conference on Cognitive and Computational Aspects of Situation Management. https://doi.org/10.1109/CogMI56440.2022.00029
2. Krendzelak, M. (2014). Machine learning and its applications in e-learning systems. Proceedings of the International Conference on Emerging eLearning Technologies and Applications. https://dx.doi.org/10.1109/ICETA.2014.7107596
3. Lei, M., & Jian, L. (2022). Influence of educational informatization based on machine learning on teaching mode. Education Research International, 2022, Article ID 6180113. https://doi.org/10.1155/2022/6180113
4. Li, Y., Meng, S., & Wang, J. (2021). Research and application of personalized learning under the background of artificial intelligence. Proceedings of the IEEE International Conference on Artificial Intelligence and Education Technology. https://doi.org/10.1109/EIMSS53851.2021.00020
5. Ma, L., & Li, J. (2022). Influence of Educational Informatization Based on Machine Learning on Teaching Mode. Education Research International. https://dx.doi.org/10.1155/2022/6180113
6. Montebello, M. (2021). Personalized learning environments. IEEE Transactions on Learning Technologies. https://doi.org/10.1109/ISET52350.2021.00036
7. Sanusi, I. T. (2021). Teaching Machine Learning in K-12 Education. Proceedings of the International Conference on Learning and Teaching Technologies. https://dx.doi.org/10.1145/3446871.3469769
8. Wang, L. (2022). Proactive push research on personalized learning resources based on machine learning. Proceedings of the IEEE International Conference on Ubiquitous Systems. https://doi.org/10.1109/ICUS55513.2022.9987163
9. Xiaoke, Z. (2021). Research on personalized learning practice of higher vocational students from the perspective of education big data. Advances in Social Science, Education and Humanities Research, 617, 654-661. https://doi.org/10.2991/assehr.k.210806.191