APPLYING MACHINE LEARNING TO PERSONALIZED EDUCATION: TAILORING CONTENT TO STUDENT NEEDS

Authors

  • MAE ANN PARDILLO Author

Keywords:

machine learning, personalized education, adaptive learning, predictive analytics, natural language processing, student engagement

Abstract

Personalized education leverages machine learning to adapt learning content and methodologies to the unique needs of individual students. This study explores the integration of machine learning algorithms in educational systems, focusing on the development of personalized learning pathways that enhance student engagement and performance. Techniques such as natural language processing, collaborative filtering, and predictive analytics are examined for their roles in customizing content delivery. Case studies highlight the impact of personalized learning systems in diverse educational contexts, from primary education to lifelong learning. The findings underscore the potential of machine learning to revolutionize education by making it more inclusive, adaptive, and effective.

 

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Published

2024-12-28

How to Cite

APPLYING MACHINE LEARNING TO PERSONALIZED EDUCATION: TAILORING CONTENT TO STUDENT NEEDS. (2024). Cognify : Journal of Artificial Intelligence and Cognitive Science, 1(4), 1-8. https://researchvision.us/index.php/cognify/article/view/146

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