Personalization of Learning: Machine Learning Models for Adapting Educational Content to Individual Learning Styles

Abstract

Given the inherent diversity in learning styles and rhythms, the current educational landscape demands continuous adaptation toward methodologies that enhance individualized learning. This study addresses the effectiveness of learning personalization using machine learning models to adapt educational content to individual learning styles. Focusing our attention on a cohort of 450 university students, we implemented classification algorithms and neural networks to diagnose learning styles and personalize educational resources accordingly. The results are revealing: the students' average grades experienced a significant increase, going from 70 to 75 points on a scale of 100 after the personalized intervention. Additionally, increased engagement was recorded, evidenced by more substantial interaction with educational materials tailored to their learning preferences. These findings suggest that personalization of learning is a powerful and effective tool that can improve both academic performance and students' educational experience. This work confirms the relevance of educational personalization supported by artificial intelligence and provides a practical model for its effective implementation. The implications of this study are particularly pertinent to the evolution of pedagogical practices and curriculum design in the digital age.