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Related Experiment Video

Updated: Sep 8, 2025

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Music Art Teaching Quality Evaluation System Based on Convolutional Neural Network.

Fumei Xu1, Yu Xia2

  • 1School of Music, Jiangxi Normal University, Nanchang Jiangxi 330027, China.

Computational and Mathematical Methods in Medicine
|June 13, 2022
PubMed
Summary
This summary is machine-generated.

Developing a robust teaching quality assessment (TQE) for music and art education is crucial. This study introduces a novel Convolutional Neural Network (CNN) model for music art TQE, demonstrating superior accuracy over traditional methods.

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Area of Science:

  • Education
  • Computer Science
  • Artificial Intelligence

Background:

  • Music and art education programs are expanding rapidly in higher education.
  • Current teaching quality assessment (TQE) methods in these fields are underdeveloped and often borrowed from other disciplines, leading to ineffective evaluations.
  • Existing TQE approaches lack rigor and fail to provide meaningful feedback for improvement in music and art education.

Purpose of the Study:

  • To address the limitations of current TQE in music and art education.
  • To develop a more appropriate and effective evaluation system for music and art teaching quality.
  • To leverage advanced computational methods for improved educational assessment.

Main Methods:

  • A comprehensive review of existing domestic and international TQE research and methods in music and art.
  • Introduction and explanation of Convolutional Neural Network (CNN) principles and architecture.
  • Construction of a TQE system specifically tailored for music and art education using CNN.

Main Results:

  • The study introduces a novel TQE method based on Convolutional Neural Network (CNN).
  • Experimental results indicate that the developed CNN model achieves higher accuracy and better performance compared to the traditional BP neural network.
  • The CNN-based TQE system demonstrates effectiveness in evaluating music and art teaching quality.

Conclusions:

  • The proposed CNN model offers a significant advancement in music and art teaching quality assessment.
  • This approach provides a more rigorous and accurate evaluation framework than existing methods.
  • The findings suggest that CNNs are well-suited for addressing nonlinear problems in educational TQE, offering improved feedback and advancement opportunities.