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

<p>In order to innovate the college music education model, this paper first analyzes the design ideas of music-diversified courses based on deep learning and carries out the feature extraction of music course content. Secondly, combined with the diversified music wisdom environment and curriculum evaluation, a diversified music curriculum system teaching model based on deep learning is designed. Finally, the teaching practice is conducted, and a control experiment is designed to analyze the effectiveness of diversified curriculum innovations. The results showed that after the teaching practice, the basic level of the experimental class increased by 0.26, the cognitive ability increased by 0.23, the learning attitude increased by 0.23, the learning style increased by 0.23, and the interests and hobbies increased by 0.25. The diversified curriculum based on this paper can effectively carry out music teaching innovation.</p>
</abstract>

In order to innovate the college music education model, this paper first analyzes the design ideas of music-diversified courses based on deep learning and carries out the feature extraction of music course content. Secondly, combined with the diversified music wisdom environment and curriculum evaluation, a diversified music curriculum system teaching model based on deep learning is designed. Finally, the teaching practice is conducted, and a control experiment is designed to analyze the effectiveness of diversified curriculum innovations. The results showed that after the teaching practice, the basic level of the experimental class increased by 0.26, the cognitive ability increased by 0.23, the learning attitude increased by 0.23, the learning style increased by 0.23, and the interests and hobbies increased by 0.25. The diversified curriculum based on this paper can effectively carry out music teaching innovation.

Diversified curriculum innovation of college music education under deep learning model

Applied Mathematics and Nonlinear Sciences

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

In order to innovate the college music education model, this paper first analyzes the design ideas of music-diversified courses based on deep learning and...

Diversified curriculum innovation of college music education under deep learning model

Data publikacji: 11 lis 2023

Zakres stron: -

Otrzymano: 17 lut 2023

Przyjęty: 22 maj 2023

DOI: https://doi.org/10.2478/amns.2023.2.01102

Słowa kluczowe
Feature extraction, Music intelligence environment, Deep learning, College music education, Diversified curriculum

© 2023 Yajing Xin, published by Sciendo

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

Diversified curriculum innovation of college music education under deep learning model

Data publikacji: 11 lis 2023

Zakres stron: -

Otrzymano: 17 lut 2023

Przyjęty: 22 maj 2023

DOI: https://doi.org/10.2478/amns.2023.2.01102

Słowa kluczoweFeature extraction, Music intelligence environment, Deep learning, College music education, Diversified curriculum

© 2023 Yajing Xin, published by Sciendo

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

Słowa kluczowe
Feature extraction, Music intelligence environment, Deep learning, College music education, Diversified curriculum