Construction of a personalized learning path for vocal music teaching through cloud platform in college music courses

Abstract

How to effectively use artificial intelligence technology to achieve the purpose of optimizing teaching resources and realizing personalized teaching has become an important research field. In this paper, with the help of development software, the functional modules, database and interface of the music teaching cloud platform are designed respectively, and the music teaching cloud platform is finally constructed. In view of the application prospect of ant colony algorithm in personalized learning path, in this regard, ant colony algorithm is introduced into the music teaching cloud platform to realize the personalized learning path planning and recommendation for vocal music teaching. Finally, the form of algorithm comparison is used to verify and analyze the method of this paper. The output results of the three algorithms are 0.7436, 0.6972, 0.6751 respectively, indicating that the priority of the ant colony algorithm is greater than the genetic algorithm and simulated annealing algorithm in the personalized learning path of vocal music teaching.