Digital Transformation and Effect Evaluation of Music Education Driven by Internet of Things Technology

In the last few years, the digital revolution has greatly influenced educational methodologies, and the Internet of Things (IoT) has been at the forefront of enlightening teaching and learning experiences. To discover the integration of IoT technology into music education, this research combines the Enhanced Northern Goshawk Optimized Deep Spiking Neural Network (ENG-DSNN) framework. The suggested approach utilizes IoT-driven devices to collect data on students’ physiological and psychological states in real time as they take music lessons, including heart rate, stress levels, engagement, and behavioral data. In addition, data normalization is carried out with Min-Max Scaling, and the acceptance of missing values is done by using Multivariate Imputation by Chained Equations (MICE). Subsequently, the use of ENG-DSNN integrates the global search ability of the ENG with the temporal learning strengths of the DSNN. ENG is then used to determine the hyperparameter bounds for giving the best possible settings for the DSNN, thereby increasing the accuracy of its predictions in assessing music education outcomes. Experimental results show the effectiveness of the IoT-enhanced music education system with 98.2% accuracy, depicting the robust performance metrics of the ENG-DSNN model. The research evaluates both the educational impact and mental health of IoT-driven music education, providing actionable insights for educators. Predictive analytics further suggest customized interventions to improve student engagement and mental health.