Big Data-Driven Analysis of Music Learning Behavior: Personalized Teaching Recommendations and Learning Strategy Optimization

This paper discusses how big data analytics could revolutionize music education by suggesting pedagogic ideas and shaping learning experiences based on student profiles. Through a combination of practice records, teacher-student dialogue and qualitative feedback, this study builds detailed portraits of music students to inform adaptive, data-informed teaching. It includes collecting, processing and analyzing the data, and applying clustering to classify the learner into Fast Learners, Methodical Learners and Emotionally Driven Learners. Each profile gets personalized practice schedules, feedback, and incentives for improved engagement and skill acquisition. Findings demonstrate that personalized methods deliver considerable improvements in learning and achievement. It also uses adaptive learning pathways and gamification to enhance student progress. Adapting to the new realities through feedback loops and constantly updated adjustments, this big data approach holds the promise of a fresh and flexible music education model. This study not only suggests how big data can drive personalized learning but offers a template for future educational data mining in music and beyond.