Personalized Learning Style Detection and Pathway Optimization Using Hybrid Machine Learning Approaches

The demand for personalized learning in education has led to the need for adaptive systems that cater to diverse student needs and learning styles. This paper proposes a hybrid framework that combines learning style detection with dynamic pathway optimization to enhance student engagement and outcomes. By leveraging data-driven techniques, the framework identifies individual preferences and adapts educational content accordingly, ensuring an effective and tailored learning experience. Through illustrative scenarios, we demonstrate how this approach can accommodate various learning styles, such as visual, auditory, and kinesthetic, while optimizing content delivery to improve satisfaction and performance. This work highlights the potential for scalable and adaptable solutions in e-learning platforms, offering significant benefits across diverse educational and training contexts. The proposed framework lays a foundation for future research in personalized education systems.