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<p>Under the background of big data development, students have diverse and multi-level demands for music aesthetic education teaching. This paper studies the implementation strategy of aesthetic education in college music teaching under the threshold of big data, mainly through data analysis, predictive modeling and simulation, multi-faceted analysis, and the design of personalized music teaching intervention. By training DNN model experimental conclusion and analysis, the DNN model in the original set R-squared reached 0.8037, the largest performance among all models, compared to multiple linear regression, in multi-level multi-neuron activation, through the sigmoid activation of each level and linear calculation between levels, in backpropagation to update the weights and bias, trained to be able to more The DNN is finally selected as the best model for music learners’ performance prediction. The DNN is finally selected as the best model for music learners’ performance prediction. The advantages of big data mining music aesthetic education resources are exploited to create an open aesthetic education teaching space for students.</p>
</abstract>

Under the background of big data development, students have diverse and multi-level demands for music aesthetic education teaching. This paper studies the implementation strategy of aesthetic education in college music teaching under the threshold of big data, mainly through data analysis, predictive modeling and simulation, multi-faceted analysis, and the design of personalized music teaching intervention. By training DNN model experimental conclusion and analysis, the DNN model in the original set R-squared reached 0.8037, the largest performance among all models, compared to multiple linear regression, in multi-level multi-neuron activation, through the sigmoid activation of each level and linear calculation between levels, in backpropagation to update the weights and bias, trained to be able to more The DNN is finally selected as the best model for music learners’ performance prediction. The DNN is finally selected as the best model for music learners’ performance prediction. The advantages of big data mining music aesthetic education resources are exploited to create an open aesthetic education teaching space for students.

Strategies for the implementation of aesthetic education in music teaching in colleges and universities under the threshold of big data

Applied Mathematics and Nonlinear Sciences

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

Under the background of big data development, students have diverse and multi-level demands for music aesthetic education teaching. This paper studies the...

Strategies for the implementation of aesthetic education in music teaching in colleges and universities under the threshold of big data

Data publikacji: 01 cze 2023

Zakres stron: -

Otrzymano: 14 lip 2022

Przyjęty: 07 gru 2022

DOI: https://doi.org/10.2478/amns.2023.1.00328

Słowa kluczowe
Big data, Music teaching, Personalized intervention teaching, DNN model, Music aesthetic education resources

© 2023 Jingjing Zhu, published by Sciendo

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

Strategies for the implementation of aesthetic education in music teaching in colleges and universities under the threshold of big data

Data publikacji: 01 cze 2023

Zakres stron: -

Otrzymano: 14 lip 2022

Przyjęty: 07 gru 2022

DOI: https://doi.org/10.2478/amns.2023.1.00328

Słowa kluczoweBig data, Music teaching, Personalized intervention teaching, DNN model, Music aesthetic education resources

© 2023 Jingjing Zhu, published by Sciendo

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

Słowa kluczowe
Big data, Music teaching, Personalized intervention teaching, DNN model, Music aesthetic education resources