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<p>Today, when quality education is increasingly emphasized, it is necessary to strengthen the strength of dance aesthetics education for students in colleges and universities. This paper establishes a PBL teaching mode for dance aesthetic education based on the PBL teaching method and designs a teaching experiment for this teaching mode. In the PBL teaching process, the optical motion capture system is used to obtain the three-dimensional data of dance movements. After processing the three-dimensional data through data conversion and normalization, the time and channel refinement fusion hypergraph convolutional network is introduced to identify the dance movements, which helps teachers to carry out the teaching of dance movements. The data were quantitatively analyzed for the effects of dance movement recognition, students’ dance skill level, state of mind, and aesthetic awareness on the effectiveness of dance movement recognition and dance aesthetic PBL teaching model. The results showed that the accuracy of the dance movement recognition model in ballet movement recognition reached 89.63%, the dance skill level of students in the experimental class increased by 7.51±0.05 points compared with that of the control class, and their aesthetic sense, aesthetic appreciation, and aesthetic creativity all showed very significant differences at the 1% level. Applying the PBL teaching method to dance teaching and aesthetic education can fully cultivate students’ aesthetic sense and promote the innovative integration of dance aesthetic education in colleges and universities.</p>
</abstract>

Today, when quality education is increasingly emphasized, it is necessary to strengthen the strength of dance aesthetics education for students in colleges and universities. This paper establishes a PBL teaching mode for dance aesthetic education based on the PBL teaching method and designs a teaching experiment for this teaching mode. In the PBL teaching process, the optical motion capture system is used to obtain the three-dimensional data of dance movements. After processing the three-dimensional data through data conversion and normalization, the time and channel refinement fusion hypergraph convolutional network is introduced to identify the dance movements, which helps teachers to carry out the teaching of dance movements. The data were quantitatively analyzed for the effects of dance movement recognition, students’ dance skill level, state of mind, and aesthetic awareness on the effectiveness of dance movement recognition and dance aesthetic PBL teaching model. The results showed that the accuracy of the dance movement recognition model in ballet movement recognition reached 89.63%, the dance skill level of students in the experimental class increased by 7.51±0.05 points compared with that of the control class, and their aesthetic sense, aesthetic appreciation, and aesthetic creativity all showed very significant differences at the 1% level. Applying the PBL teaching method to dance teaching and aesthetic education can fully cultivate students’ aesthetic sense and promote the innovative integration of dance aesthetic education in colleges and universities.

Reform and Innovation of Dance Teaching and Aesthetic Education in Colleges and Universities in the Digital Media Era

School of Art ZHEJIANG YUEXIU UNIVERSITY Shaoxing

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Today, when quality education is increasingly emphasized, it is necessary to strengthen the strength of dance aesthetics education for students in colleges and...

Reform and Innovation of Dance Teaching and Aesthetic Education in Colleges and Universities in the Digital Media Era

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 04 gen 2024

Accettato: 10 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0356

Parole chiave
Motion capture, Three-dimensional data, Hypergraph convolutional network, TC-HGCN model, Dance teaching

© 2024 Qian Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Reform and Innovation of Dance Teaching and Aesthetic Education in Colleges and Universities in the Digital Media Era

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 04 gen 2024

Accettato: 10 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0356

Parole chiaveMotion capture, Three-dimensional data, Hypergraph convolutional network, TC-HGCN model, Dance teaching

© 2024 Qian Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Motion capture, Three-dimensional data, Hypergraph convolutional network, TC-HGCN model, Dance teaching