Deep learning-based personalized learning recommendation system design for "T++" Guzheng Pedagogy

This study investigates the development and impact of a deep learning-based personalized learning recommendation system designed specifically for 'T++' Guzheng pedagogy. In the realm of music education, particularly in the context of the traditional Chinese Guzheng instrument, technology-driven personalization has the potential to revolutionize learning experiences. The research involves data collection, algorithm development, and integration to create a system that tailors Guzheng learning materials to individual students' skill levels and preferences. Results indicate high recommendation accuracy, increased user satisfaction, and positive engagement rates. This study contributes to the intersection of technology, music education, and cultural preservation, showcasing the promise of personalized learning in the realm of Guzheng pedagogy.