Innovating and Evaluating Music Education Materials through Music Cognition and Bioinformatics

Abstract

This paper embarks on an interdisciplinary exploration, melding bioinformatics methodologies with music education to enhance the development and evaluation of music teaching materials. Initially, it provides an overview of the current landscape of music education within the context of contemporary informatics, identifying the prevailing challenges in existing music education resources. The study then advances the discourse by refining the application of Independent Component Analysis (ICA) and clustering techniques, introducing an integrated approach that combines wavelet packet decomposition with ICA and clustering for the analysis of electroencephalography (EEG) signals related to music cognition. This novel strategy aims to unravel the intricate relationship between music cognition and the effectiveness of music education materials. Leveraging the insights gleaned from music cognition studies, the research further delves into the use of bioinformatics tools to innovate in creating and assessing music education content. Through comparative experiments, this study evaluates the impact of these innovations on music teaching materials. The findings reveal a notable enhancement in the EEG signal responses to music cognition with the updated materials, exhibiting a 0.30 improvement. Additionally, the utilization of the revised teaching aids resulted in significant gains in music identity, emotion, and performance by approximately 0.25, alongside a 0.23 increase in student engagement and motivation towards music learning. By integrating bioinformatics approaches with music cognitive studies, this paper presents a pioneering model for the innovation of music education materials. The outcomes underscore the potential of this interdisciplinary method to elevate the quality of music teaching and underscore its substantial value in the realm of music education research.