The innovation path of VR technology integration into music classroom teaching in colleges and universities - Yupeng Han; Lin Han; Chun Zeng; Wei Zhao (Scientific Reports)
Abstract Traditional music education in higher education institutions has traditionally followed a one-size-fits-all teaching model, which limits student interaction and hinders personalized learning. This approach does not align with the expectations of modern students, who seek a more engaging and effective learning experience. With the growing integration of Virtual Reality (VR) technology in education, its immersive and interactive features offer new possibilities for enhancing music instruction in colleges and universities. To explore these possibilities, this study proposes an Intelligent Interactive Music Teaching (IIMT) model that combines VR technology with Deep Convolutional Generative Adversarial Networks and Deep Deterministic Policy Gradient algorithms. The study utilizes publicly available music teaching videos and virtual environment interaction data. After applying data cleaning, noise reduction, and normalization techniques, the processed data is used to construct training and validation datasets. Experimental results indicate that the IIMT model generates images and audio with detail richness and clarity scores ranging from 0.7 to 1.0. The optimized system maintains a response time between 85 and 115 milliseconds and an average frame rate of 55 to 65 frames per second, ensuring smooth interaction. In a “vocal training” scenario, the IIMT model achieves an efficiency score of 0.96 and a task completion rate of 98.77%, demonstrating its effectiveness in improving instructional quality and enhancing students’ learning experiences. These findings suggest that the IIMT model can serve as a valuable tool for educators and institutions seeking to modernize music education through interactive and intelligent teaching methodologies.