Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

January 31, 2024


Pages


DOI

Article

An Exploration of Ethnic Dance Teaching Methods in Dance Performance Teaching in Colleges and Universities in the Context of Big Data

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Authors

Liyang He Affiliation:
School of Dance, Shanghai Theatre Academy, Shanghai, 200000, China.


Abstract

In this paper, based on the practical framework of teaching folk dance in colleges and universities under the background of big data, the spatial and temporal characteristics of folk dance are sorted out, and the DTW algorithm is proposed to calculate the similarity of folk dance movements, and a model for estimating the gesture of normative movements of folk dance is constructed by combining multiscale feature fusion. Six kinds of folk dance movements were collected to establish the data samples of folk dance movements in order to verify the effectiveness of the calculation method of dance movement similarity and to obtain the results of the teaching of folk dance normative movements. Analyze the content of dance movements and the dance normative movement gesture estimation method to explore the expressiveness of folk dance movements. To research the effectiveness of emotional dance movement teaching in teaching folk dance in two dimensions, three segments of folk dance were chosen. The data show that the second segment of the dance, M=9.45, SD=2.15, was improved to M=11.55, SD=3.16. Affective dance movement teaching has a significant impact on the teaching of folk dance in colleges and universities, with improvements in both dance movement and dance expression.


Keywords

DTW algorithm, Movement gesture estimation, Similarity calculation, Folk dance teaching, 11N36


Citation

He, L. (2024). An exploration of ethnic dance teaching methods in dance performance teaching in colleges and universities in the context of big data. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-0097
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