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

<p>Today, the evolution of art teaching evaluation systems faces significant challenges and opportunities, particularly with the limitations of subjective judgments in traditional methods impacting the accuracy of teaching quality assessments. This study leverages advancements in artificial intelligence, specifically neural networks enhanced by the Taruhai Sheath algorithm, to propose a novel and more objective approach for evaluating art teaching. Our optimized neural network model demonstrates a remarkable 92% accuracy in art teaching evaluations, outperforming traditional methods by 15%. Furthermore, our analysis reveals that the quality of practical teaching is a critical factor in boosting students’ artistic creativity. By applying the Taruhai Sheath algorithm for neural network optimization, this research offers a groundbreaking method to elevate the objectivity and rigor of art education evaluations, promising a significant leap forward in art teaching quality.</p>
</abstract>

Today, the evolution of art teaching evaluation systems faces significant challenges and opportunities, particularly with the limitations of subjective judgments in traditional methods impacting the accuracy of teaching quality assessments. This study leverages advancements in artificial intelligence, specifically neural networks enhanced by the Taruhai Sheath algorithm, to propose a novel and more objective approach for evaluating art teaching. Our optimized neural network model demonstrates a remarkable 92% accuracy in art teaching evaluations, outperforming traditional methods by 15%. Furthermore, our analysis reveals that the quality of practical teaching is a critical factor in boosting students’ artistic creativity. By applying the Taruhai Sheath algorithm for neural network optimization, this research offers a groundbreaking method to elevate the objectivity and rigor of art education evaluations, promising a significant leap forward in art teaching quality.

Research on the Evaluation of Art Teaching Based on Optimized Neural Network of Bottle Sea Sheath Algorithm

Applied Mathematics and Nonlinear Sciences

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

Today, the evolution of art teaching evaluation systems faces significant challenges and opportunities, particularly with the limitations of subjective...

Research on the Evaluation of Art Teaching Based on Optimized Neural Network of Bottle Sea Sheath Algorithm

Publicado en línea: 03 may 2024

Páginas: -

Recibido: 05 abr 2024

Aceptado: 16 abr 2024

DOI: https://doi.org/10.2478/amns-2024-0872

Palabras clave
Taruhai Sheath Algorithm, Neural Network Optimization, Art Teaching Evaluation, Multidimensional Evaluation Indicators

© 2024 Xuedan Liu et al., published by Sciendo

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

Research on the Evaluation of Art Teaching Based on Optimized Neural Network of Bottle Sea Sheath Algorithm

Publicado en línea: 03 may 2024

Páginas: -

Recibido: 05 abr 2024

Aceptado: 16 abr 2024

DOI: https://doi.org/10.2478/amns-2024-0872

Palabras claveTaruhai Sheath Algorithm, Neural Network Optimization, Art Teaching Evaluation, Multidimensional Evaluation Indicators

© 2024 Xuedan Liu et al., published by Sciendo

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

Palabras clave
Taruhai Sheath Algorithm, Neural Network Optimization, Art Teaching Evaluation, Multidimensional Evaluation Indicators