Constructing and evaluating the effects of an immersive teaching mode for art education based on machine learning

Abstract

As art education increasingly emphasizes personalized and experiential learning, traditional teaching methods are in need of reform. Immersive teaching modes, by offering highly interactive and personalized learning experiences, can enhance students’ artistic practice abilities and esthetic appreciation. This study aims to construct an immersive teaching mode for art education based on machine learning and to evaluate and adjust its effectiveness. Initially, we developed a set of personalized recommendation strategies using sentiment analysis, knowledge graphs, and learner interaction data to build an art teaching mode tailored to different students’ needs. Subsequently, we employed the k-medoids clustering algorithm and convolutional neural networks (CNN) to comprehensively assess the effectiveness of the immersive teaching mode and optimize the teaching mode using the results of sentiment analysis. Existing research in the personalization and intelligence of art education remains insufficient; the machine learning approach provided in this study better understands students’ needs and feedback, allowing for the personalized customization of teaching content and strategies. The results indicate that this teaching mode effectively improves students' art learning outcomes and satisfaction, offering new insights and tools for the modern development of art education. The innovation of this paper lies in the construction of an immersive teaching model for art education based on sentiment analysis, knowledge graphs, and learner interaction data. This model is evaluated and dynamically adjusted using the k-medoids clustering algorithm and CNN.