Deep learning perspective on the construction of SPOC teaching model of music and dance in colleges and universities

Abstract

In the era of knowledge economy, social development has higher requirements for artistic talents’ quality. The teaching mode of music and dance in colleges and universities needs to be changed urgently. This study introduced deep learning theory and designed a SPOC teaching mode for music and dance in universities to improve the quality of music and dance teaching. Firstly, the K-means clustering algorithm was used to extract the features of online learning behavior. Radial basis function neural network was used to predict students’ performance to improve teachers’ mastery of students’ online learning behavior. Subsequently, the teaching quality evaluation system was constructed using hierarchical analysis. Finally, the effectiveness of the teaching model was tested. The results showed that under this teaching model, students’ overall literacy score reached a maximum of 11.5, and their average score in music and dance reached a maximum of 94, an increase of 11 points compared to the SPOC teaching mode. This indicates that the teaching mode gives full play to the advantages of SPOC teaching, enhances students’ enthusiasm and initiative in learning, and improves their professionalism and comprehensive quality.