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

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.