IEC Driven Guitar Etude Optimization System Considering Learner Preferences and Skills

Abstract

While interest in playing musical instruments continues to grow, self-directed learning remains challenging, leading many learners to abandon their practice. Previous research on musical instrument learning support has primarily focused on performance evaluation and feedback, with insufficient attention given to practice materials themselves. This study proposes an Interactive Evolutionary Computation (IEC) based system that optimizes guitar practice materials according to individual learner preferences and skill levels to enhance learning motivation. Through experimental evaluation with 15 participants, we found that approximately 87% reported increased motivation when practicing with personalized materials. These results suggest that tailoring practice materials to individual preferences and abilities can significantly improve learning motivation for musical instrument learners.