Analysis of the Impact of Information Intelligent Teaching Methods on the Teaching Effect of Instrumental Music: Student Group Division Based on Clustering Algorithm

Abstract

This research paper explores the effects of information intelligent teaching methods on instrumental music instruction, focusing on student group division through clustering algorithms. In the realm of music education, personalized learning experiences are crucial for enhancing teaching effectiveness and student engagement. Leveraging data-driven approaches, particularly clustering algorithms, they categorize students into homogeneous groups based on their musical abilities, learning preferences, and aptitudes. Through empirical analysis, they assess the impact of these group divisions on teaching effectiveness and student learning outcomes. By investigating the implications of information intelligent teaching methods, this study sheds light on innovative pedagogical practices in instrumental music education and their potential to transform traditional teaching paradigms.