Analysis of Virtual Reality-based Music Education Experience and its Impact on Learning Outcomes

Abstract

This study aims to analyze the impact of virtual reality (VR)-based music education on learning outcomes, integrating the strengths of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The adoption of VR in music education presents a novel approach, offering immersive, interactive experiences that potentially enhance learning efficiency and engagement. Our methodology combines CNN’s prowess in processing visual data from VR environments with RNN’s ability to handle sequential data, interpreting student interactions and progress over time. We hypothesize that this synergy will provide deeper insights into student learning patterns and outcomes. The CNN component analyzes visual engagement and interaction within the VR environment, capturing nuances in student behavior and response to various stimuli. Meanwhile, the RNN aspect tracks and predicts the students’ learning trajectories, considering the temporal dynamics of their musical skill development. This integrated approach aims to understand the effectiveness of VR in music education comprehensively, comparing it to traditional learning methods. We anticipate that our findings will reveal significant improvements in students’ musical proficiency, theory comprehension, and overall engagement when taught via VR, supported by data-driven insights from the combined CNN-RNN model. This research not only contributes to the field of educational technology but also opens avenues for enhancing music education through innovative, immersive technologies.