Virtual Reality and Internet of Things-Based Music Online Learning via the Graph Neural Network

Abstract

Virtual reality and the Internet of Things have shown their capability in a variety of tasks. However, their availability in online learning remains an unresolved issue. To bridge this gap, we propose a virtual reality and Internet of Things-based pipeline for online music learning. The one graph network is used to generate an automated evaluation of learning performance which traditionally was given by the teachers. To be specific, a graph neural network-based algorithm is employed to identify the real-time status of each student within an online class. In the proposed algorithm, the characteristics of each student collected from the multisensors deployed on their bodies are taken as the input feature for each node in the presented graph neural network. With the adoption of convolutional layers and dense layers as well as the similarity between each pair of students, the proposed approach can predict the future circumstance of the entire class. To evaluate the performance of our work, comparison experiments between several state-of-the-art algorithms and the proposed algorithm were conducted. The result from the experiments demonstrated that the graph neural network-based algorithm achieved competitive performance (sensitivity 91.24%, specificity 93.58%, and accuracy 89.79%) over the state-of-the-art.

Design and Development of e-MARZ: A Practical Guide to Implementing Cost-effective VR Experiences for Music Education

“…For instance, Han, [9] proposed a constructivism-based vocal music education system using the Moodle platform and VR technology to enhance the intuitiveness and interactivity of lessons. Similarly, Lian et al, [10] presented a VR and IoT-based pipeline for online music learning using a graph neural network (GNN) algorithm, which displayed competitive performance compared to state-of-the-art methods. Furthermore, Sai, [11] conducted a study comparing VR-supported learning using the VR4EDU application to traditional online music lessons, demonstrating statistically significant improvements in the former .…”