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Research on the Diversity of Technical Characteristics of Folk Music Classroom Teaching Based on Network Knowledge Sharing

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e-Learning, e-Education, and Online Training (eLEOT 2021)

Abstract

Aiming at the problem of poor convergence in the research method of feature diversity of music teaching technology, this paper proposes a research on the diversity of folk music classroom teaching technology based on network knowledge sharing. According to the classical viewpoints of international authoritative organizations and domestic and foreign literatures, the evaluation index system is constructed. Analytic hierarchy process (AHP) and Delphi method are introduced to analyze the weight of the evaluation index system. Through network knowledge sharing, the membership degree of teaching technical characteristics is calculated, and the diversity of teaching technical characteristics of folk music is judged. This study makes a comparative experiment on the three methods and analyzes the diversity of teaching techniques of folk music in the classroom. Experimental results show that the proposed method has the advantages of stable application process, short time and good convergence performance.

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Funding

Youth project of Humanities and social sciences research in Colleges and universities of Jiangxi Province: Research on the music form of “Quanfeng lantern” in Northwest Jiangxi (NO. YS20206).

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Lu, L., Zhou, J., Wu, Yb. (2021). Research on the Diversity of Technical Characteristics of Folk Music Classroom Teaching Based on Network Knowledge Sharing. In: Fu, W., Liu, S., Dai, J. (eds) e-Learning, e-Education, and Online Training. eLEOT 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 390. Springer, Cham. https://doi.org/10.1007/978-3-030-84386-1_43

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  • DOI: https://doi.org/10.1007/978-3-030-84386-1_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84385-4

  • Online ISBN: 978-3-030-84386-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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