Enhancing dance education through convolutional neural networks and blended learning.

ABSTRACT: This article explores the evolving landscape of dance teaching, acknowledging the transformative impact of the internet and technology. With the emergence of online platforms, dance education is no longer confined to physical classrooms but can extend to virtual spaces, facilitating a more flexible and accessible learning experience. Blended learning, integrating traditional offline methods and online resources, offers a versatile approach that transcends geographical and temporal constraints. The article highlights the utilization of the dual-wing harmonium (DWH) multi-view metric learning (MVML) algorithm for facial emotion recognition, enhancing the assessment of students' emotional expression in dance performances. Moreover, the integration of motion capture technology with convolution

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC11622838 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature