<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study proposes an intelligent teaching model for music that incorporates traditional music culture and scientifically evaluates learning outcomes through a simulated annealing algorithm. The algorithm determines the initial temperature by adaptive function transformation, which enhances its applicability and generalization. The music intelligent teaching model is applied in actual teaching and data are collected by comparing and analyzing the learning outcomes of two parallel classes using questionnaires and statistical methods. The results of the experiment showed that the mean scores of the students in the experimental class were significantly better than those of the control class in terms of linguistic intelligence (P=0.042) and musical intelligence (P=0.003), indicating the effectiveness of the teaching model. The most significant advantage of the model is that it allows students to learn anytime, anywhere, free from the constraints of time and space, which amounted to 60.8%. However, its main limitation was the tonal differences in digital course recordings. Overall, this study provides new ideas and methods for intelligent teaching of music integrating traditional cultural elements, which is of great significance for improving the quality of education.</p>
</abstract>

This study proposes an intelligent teaching model for music that incorporates traditional music culture and scientifically evaluates learning outcomes through a simulated annealing algorithm. The algorithm determines the initial temperature by adaptive function transformation, which enhances its applicability and generalization. The music intelligent teaching model is applied in actual teaching and data are collected by comparing and analyzing the learning outcomes of two parallel classes using questionnaires and statistical methods. The results of the experiment showed that the mean scores of the students in the experimental class were significantly better than those of the control class in terms of linguistic intelligence (P=0.042) and musical intelligence (P=0.003), indicating the effectiveness of the teaching model. The most significant advantage of the model is that it allows students to learn anytime, anywhere, free from the constraints of time and space, which amounted to 60.8%. However, its main limitation was the tonal differences in digital course recordings. Overall, this study provides new ideas and methods for intelligent teaching of music integrating traditional cultural elements, which is of great significance for improving the quality of education.

Digital Innovation Path of Traditional Music Culture into Modern Informational Music Teaching

Applied Mathematics and Nonlinear Sciences

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

This study proposes an intelligent teaching model for music that incorporates traditional music culture and scientifically evaluates learning outcomes through...

Digital Innovation Path of Traditional Music Culture into Modern Informational Music Teaching

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 04 sty 2024

Przyjęty: 13 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0393

Słowa kluczowe
Traditional music, Intelligent teaching, Attention mechanism, Gated loop unit

© 2024 Chunfeng Zhang, published by Sciendo

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

Digital Innovation Path of Traditional Music Culture into Modern Informational Music Teaching

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 04 sty 2024

Przyjęty: 13 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0393

Słowa kluczoweTraditional music, Intelligent teaching, Attention mechanism, Gated loop unit

© 2024 Chunfeng Zhang, published by Sciendo

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

Słowa kluczowe
Traditional music, Intelligent teaching, Attention mechanism, Gated loop unit