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

<p>This paper takes the problems and ways of diversified curriculum construction of vocal music education in colleges and universities as the entry point and constructs a deep blended learning model of vocal music diversified curriculum based on the deep learning model. The deep evolutionary knowledge tracking model is constructed by combining the self-attention mechanism embedding model with vocal music knowledge evolution based on Transformer. To verify the effectiveness of the deep blended learning model constructed in this paper, model comparison experiments and empirical analysis were conducted. The results show that compared with the SAKT model, the DBL model in this paper has a 5.19% improvement in the average ROC-AUC value. The vocal posttest scores of the students in the experimental class were 5.09 points higher than those of the control class, with a two-tailed significance value of 0.027, which is less than 0.05 and a significant difference. This indicates that the deep learning model can effectively promote the innovative design of diversified curricula for vocal music education in colleges and universities and enhance students’ vocal music learning ability.</p>
</abstract>

This paper takes the problems and ways of diversified curriculum construction of vocal music education in colleges and universities as the entry point and constructs a deep blended learning model of vocal music diversified curriculum based on the deep learning model. The deep evolutionary knowledge tracking model is constructed by combining the self-attention mechanism embedding model with vocal music knowledge evolution based on Transformer. To verify the effectiveness of the deep blended learning model constructed in this paper, model comparison experiments and empirical analysis were conducted. The results show that compared with the SAKT model, the DBL model in this paper has a 5.19% improvement in the average ROC-AUC value. The vocal posttest scores of the students in the experimental class were 5.09 points higher than those of the control class, with a two-tailed significance value of 0.027, which is less than 0.05 and a significant difference. This indicates that the deep learning model can effectively promote the innovative design of diversified curricula for vocal music education in colleges and universities and enhance students’ vocal music learning ability.

Diversified Curriculum Innovation in College Vocal Music Education under Deep Learning Modeling

Applied Mathematics and Nonlinear Sciences

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

This paper takes the problems and ways of diversified curriculum construction of vocal music education in colleges and universities as the entry point and...

Diversified Curriculum Innovation in College Vocal Music Education under Deep Learning Modeling

Pubblicato online: 11 nov 2023

Pagine: -

Ricevuto: 25 dic 2022

Accettato: 22 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.01069

Parole chiave
Vocal education, Deep blended learning, Self-attention mechanism, Transformer, Diversified curriculum

© 2023 Wei Hou, published by Sciendo

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

Diversified Curriculum Innovation in College Vocal Music Education under Deep Learning Modeling

Pubblicato online: 11 nov 2023

Pagine: -

Ricevuto: 25 dic 2022

Accettato: 22 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.01069

Parole chiaveVocal education, Deep blended learning, Self-attention mechanism, Transformer, Diversified curriculum

© 2023 Wei Hou, published by Sciendo

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

Parole chiave
Vocal education, Deep blended learning, Self-attention mechanism, Transformer, Diversified curriculum