Construction of a music education platform based on the Internet of Things and evaluation of teaching effectiveness

Music education, a must-have for a complete curriculum, still holds the ground despite the technical innovations of the digital age. The Internet of Things (IoT) has the potential to make a remarkable transformation in music education due to its capability of improving accessibility, enhancing learning experiences and providing realtime interaction among students, teachers, and musical instruments. The study intends to create a music education platform based on IoT and evaluate the teaching of music in terms of its effectiveness. In the past years, music education was faced with technical difficulties that were due to IoT like data privacy and security issues. To tackle this problem, the research proposes an Enhanced Coyote Optimizer with Resilient Decision Tree (ECO-RDT) model which aims to get a better learning experience and teaching evaluation. The learning environment that the platform offers through the integration of sensors and IoT-enabled musical instruments is one that is interactive and adaptable. The music education program was strategically placed, skill development, engagement, and performance outcomes being the main foci and instrument data collection was made in terms of Pitch, rhythm, and dynamics. The signals in the data were pre-processed first by band-pass filtering to get rid of the noise. Then, the gamma tone frequency cepstral coefficient (GFCC) is applied to obtain sound features from the preprocessed data. ECO is used to fine-tune the parameters which not only speeds up its convergence but also increases its robustness, while RDT is employed for the assessment of teaching effectiveness. To assess teaching efficacy, the proposed system compares real-time performance data with current teaching algorithms, achieving very precise results in differentiating student performance as the 10 -fold cross-validation average of the proposed method (0.965604). This innovative approach drastically boosts teaching effectiveness and student engagement and provides new insights into the future of intelligent music education.