Exploring the blended learning model of music teaching skills for teacher trainees based on teacher training professional certification in the context of big data

• Published in: Applied mathematics and nonlinear sciences Vol. 9; no. 1
• Main Author: Liu, Ke
• Format: Journal Article
• Language: English
• Published: Sciendo 01-01-2024
• Subjects: 00A65; Blended learning model; Educational data mining; Music teaching; PSO particle swarm; PSO-XGBoost algorithm
• ISSN: 2444-8656; 2444-8656
• DOI: 10.2478/amns.2023.2.00574

The existing teaching model has the problems of a single learning mode and students’ interest in learning, so it is necessary to build a blended learning model scenario to promote students’ interest in learning. This paper proposes an educational data mining algorithm for the blended learning model of music teaching skills in the context of big data and proposes a blended learning model of teaching skills based on the PSO algorithm. Since the PSO particle swarm algorithm has the problem of low accuracy and overfitting, the XGBoost algorithm model is introduced based on the PSO particle swarm algorithm, and the Iris dataset is clustered. For the evaluation analysis of the blended learning model of music skills, 13 indicators were observed for three randomly selected students in this study. The accuracy of the optimized PSO-XGBoost algorithm was 0.95, which was 10% more accurate than the pre-optimized algorithm, and the overall accuracy was significantly improved. The three students scored 18.96, 18.97, and 19.61 in the music skills blended learning model evaluation learning assessment system evaluation, with the highest score reaching 19.61. The study showed that the music-teaching blended learning model is comprehensive and easy to implement in accordance with the existing teaching environment.