Application and Effect Evaluation of Blended Teaching Model of Higher Vocational Music Education in the Context of AI Era

Abstract

In the music education industry, the use of artificial intelligence technology has greatly enriched education and teaching, education and training, music APP, intelligent systems and other aspects, injecting fresh blood into teaching activities. Based on this background, the study proposes a hybrid teaching model for English education by combining online and offline learning modes. Aiming at the deficiencies of collaborative filtering algorithm and neurocognitive diagnosis, a PMF-C&RM learning resource recommendation method integrating cognitive diagnosis and collaborative filtering is proposed, and relevant experiments are set up for verification. Taking 80 people in the English major 2024 music education as research subjects, divided into experimental and control classes, the teaching practice method was used to teach the course in four stages. The research results show that the PMF-C&RM method achieves the optimal value compared with other models, with the highest F1 value of 0.928 for simple questions and 0.932 for difficult questions, which effectively improves the effect of learning resources recommendation. Analyzing the teaching effectiveness of hybrid teaching based on PMF-C&RM in music courses, the teaching model proposed in this paper is significant in improving students' comprehensive performance ability in music.