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

<p>This paper proposes a music course resource recommendation algorithm by combining the similarity calculation of music courses with the user interest model. The ID3 algorithm in data mining is used to process the collected music course resource data, combined with the machine learning method to realize the parsing of the music course resource files and build a music course resource-sharing platform for colleges and universities based on artificial intelligence technology. Using the platform data, the correlation analysis of the factors affecting the learning effectiveness of music courses in colleges and universities was conducted, and the impact of the number of exercises on the learning effectiveness of music courses was explored. Ten colleges and universities in Guangdong Province were selected to conduct descriptive analyses of college music curriculum resources and music education teaching effectiveness based on the results of music curriculum resources development and utilization, respectively. The results show that the mean values of the dimensions of curriculum resources, text resources, and environmental resources of music education in Guangdong Province are close to the theoretical median, in which the minimum value of human resources is 1.24, the maximum value is 4.27, and the mean value is 2.765. The human resources situation is not good. Diversified teaching should focus on strengthening the human resource component, to improve the teaching effectiveness of music course resources in colleges and universities.</p>
</abstract>

This paper proposes a music course resource recommendation algorithm by combining the similarity calculation of music courses with the user interest model. The ID3 algorithm in data mining is used to process the collected music course resource data, combined with the machine learning method to realize the parsing of the music course resource files and build a music course resource-sharing platform for colleges and universities based on artificial intelligence technology. Using the platform data, the correlation analysis of the factors affecting the learning effectiveness of music courses in colleges and universities was conducted, and the impact of the number of exercises on the learning effectiveness of music courses was explored. Ten colleges and universities in Guangdong Province were selected to conduct descriptive analyses of college music curriculum resources and music education teaching effectiveness based on the results of music curriculum resources development and utilization, respectively. The results show that the mean values of the dimensions of curriculum resources, text resources, and environmental resources of music education in Guangdong Province are close to the theoretical median, in which the minimum value of human resources is 1.24, the maximum value is 4.27, and the mean value is 2.765. The human resources situation is not good. Diversified teaching should focus on strengthening the human resource component, to improve the teaching effectiveness of music course resources in colleges and universities.

Research on Diversified Diversified Teaching Strategies for Music Courses in Colleges and Universities under the Background of Artificial Intelligence

Applied Mathematics and Nonlinear Sciences

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

This paper proposes a music course resource recommendation algorithm by combining the similarity calculation of music courses with the user interest model. The...

Research on Diversified Diversified Teaching Strategies for Music Courses in Colleges and Universities under the Background of Artificial Intelligence

Data publikacji: 31 sty 2024

Zakres stron: -

Otrzymano: 20 gru 2023

Przyjęty: 25 gru 2023

DOI: https://doi.org/10.2478/amns-2024-0212

Słowa kluczowe
Course resource recommendation, Course similarity, Artificial intelligence technology, College music course

© 2024 Lei Wan, published by Sciendo

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

Research on Diversified Diversified Teaching Strategies for Music Courses in Colleges and Universities under the Background of Artificial Intelligence

Data publikacji: 31 sty 2024

Zakres stron: -

Otrzymano: 20 gru 2023

Przyjęty: 25 gru 2023

DOI: https://doi.org/10.2478/amns-2024-0212

Słowa kluczoweCourse resource recommendation, Course similarity, Artificial intelligence technology, College music course

© 2024 Lei Wan, published by Sciendo

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

Słowa kluczowe
Course resource recommendation, Course similarity, Artificial intelligence technology, College music course