In the contemporary educational environment, the protection and inheritance of traditional music culture faces challenges. The current diverse needs of music education urgently require innovative teaching models to integrate traditional music culture and enhance students' aesthetic and cultural literacy. The eco-beauty perspective provides a new educational idea that emphasizes the harmonious coexistence with nature and promotes all-around development of students. Combining traditional music culture and modern education technology effectively can be achieved by combining deep learning technology and new paths in music education. This study explores the music teaching model that integrates traditional music culture with deep learning technology, under the perspective of ecological aesthetic education. The study utilizes a mixed methodology, combines quantitative and qualitative Analysis, and compares the experimental design to verify the effectiveness of the teaching model. In this paper, implementing the music teaching model under the perspective of ecological aesthetic education has improved students' performance in music perception ability, music emotion expression, music composition and performance, and music knowledge theory. The average increase in music perception ability of the experimental class students is 5.18 points, and the average increase in music composition and performance ability is 11.57 points, showing the impact of this teaching mode on the overall improvement of students' music ability. In this paper, the music teaching mode combining deep learning under the perspective of ecological aesthetic education can effectively integrate traditional music culture, promote the improvement of students' music literacy, and provide a new teaching strategy for the inheritance and development of traditional music culture.
CITATION STYLE
Shen, Y. (2024). A Music Teaching Model Combining Deep Learning in the Perspective of Ecological Aesthetic Education: The Integration of Traditional Music Culture. Applied Mathematics and Nonlinear Sciences, 9(1), 1–14. https://doi.org/10.2478/amns-2024-0880
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