Year: 2025
Type: article
Abstract: To address the subjectivity and uncertainty in traditional music teaching quality evaluation methods, a method based on the PSO-BP neural network model is proposed. This method selects evaluation indicators from multiple dimensions such as teaching ability, learning effectiveness, teaching resources, teaching interaction, and music emotion. By optimizing the initial connection weights and thresholds of the BP neural network through PSO algorithm, an objective, accurate, and efficient music teaching quality evaluation system is constructed. Compared to traditional methods, it not only significantly enhances objectivity and accuracy but also further refines the evaluation criteria by introducing multi-level evaluation dimensions such as technicality, motivation, teaching, interactivity, artistry, and standardization. The case analysis shows that this method performs well in practical applications, and the evaluation results are closer to reality, providing strong support for the improvement of music teaching quality.
Source: Discover Artificial Intelligence
Author: Keyi Zhu
Institution: Sanya University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W4415915200