Applied Mathematics and Nonlinear Sciences (Jan 2024)
Innovative Path of Music Education Teaching in Colleges and Universities under the Architecture of Disciplinary Knowledge Mapping
Abstract
The purpose of this paper is to present a pedagogical model for mapping subject knowledge in the field of music education. The model includes two aspects: the extraction of entities and inter-entity relationships within the subject domain and the construction of a subject ontology. In the construction process of subject knowledge mapping, this paper proposes a remote supervised music subject knowledge extraction method based on a convolutional neural network combined with an attention mechanism, which realizes the extraction of entities in the music subject domain and the relations between corresponding entities. This paper proposes a keyword extraction method for obtaining the set of discipline concepts in ontology construction. To improve performance, this paper proposes a framework structure for knowledge fusion that includes three aspects: data preprocessing, similarity calculation, and knowledge fusion. As a result of the study, the teaching model can improve the performance of music majors on average by 13.3. In terms of self-efficacy, on average reached 3.58 (SD = 0.535), which is at a good level. The results demonstrated the effectiveness of the music teaching model based on subject knowledge mapping.
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