<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Innovative Path of Music Education Teaching in Colleges and Universities under the Architecture of Disciplinary Knowledge Mapping

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

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...

Innovative Path of Music Education Teaching in Colleges and Universities under the Architecture of Disciplinary Knowledge Mapping

Online veröffentlicht: 25. Nov. 2023

Seitenbereich: -

Eingereicht: 10. Jan. 2023

Akzeptiert: 05. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01220

Schlüsselwörter
Subject knowledge mapping, Convolutional neural networks, Attention mechanisms, Subject ontology construction, Music education

© 2023 Jiexia Wu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Innovative Path of Music Education Teaching in Colleges and Universities under the Architecture of Disciplinary Knowledge Mapping

Online veröffentlicht: 25. Nov. 2023

Seitenbereich: -

Eingereicht: 10. Jan. 2023

Akzeptiert: 05. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01220

SchlüsselwörterSubject knowledge mapping, Convolutional neural networks, Attention mechanisms, Subject ontology construction, Music education

© 2023 Jiexia Wu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Subject knowledge mapping, Convolutional neural networks, Attention mechanisms, Subject ontology construction, Music education