Fostering Creativity in Fine Arts Education Through Hybrid Learning Models

Abstract

This study examines the possibilities of hybrid learning models to inspire creativity in fine arts education, utilizing personalized learning technologies: Collaborative Filtering and K-Nearest Neighbors (KNN) algorithms. Hybrid learning, which blends online instruction with face-to-face interaction, offers an elastic environment that may support various learning styles and foster greater engagement by students. The research explores how such technologies can personalize learning, provide relevant content recommendations, and challenge students at a proper level to enhance their creativity and artistic expression. This study used a mixed-methods approach to determine student creativity, engagement, and satisfaction through pre-and post-assessments, as well as algorithmic evaluations of content recommendation accuracy. Results show a considerable increase in student engagement at 27%, an improvement in creativity at 15%, and satisfaction at 28% among students who followed hybrid learning compared to traditional learning methods. This study emphasizes the effectiveness of personalized pathways in fine arts education, concluding that hybrid models can enhance the development of creativity and achievement in academic performance. These findings point to the potential of embedding personalized technologies into fine arts curricula, revolutionizing art education and making it more adaptive, inclusive, and supportive of diverse student needs.