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Application of Optimized GA-BPNN Algorithm in English Teaching Quality Evaluation System.

Yaowu Zhu1,2, Junnong Xu3, Sihong Zhang4

  • 1Editorial Office of the Journal, Anhui Vocational College of City Management, Hefei 230011, China.

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An optimized algorithm combining genetic algorithms (GA) and backpropagation neural networks (BPNN) significantly improves teaching quality assessment accuracy. This novel approach offers more reliable and scientific evaluation results for educational systems.

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Area of Science:

  • Educational Assessment
  • Artificial Intelligence in Education
  • Machine Learning for Quality Management

Background:

  • Traditional teaching quality assessment methods are insufficient due to the complexity and nonlinearity of the evaluation process.
  • Numerous factors and variables complicate mathematical modeling for teaching quality.
  • A need exists for more effective and accurate methods to evaluate teaching quality.

Purpose of the Study:

  • To propose an optimized Genetic Algorithm-Backpropagation Neural Network (GA-BPNN) algorithm for enhanced teaching quality evaluation.
  • To develop a robust system for assessing English teaching quality.
  • To improve the accuracy and scientific validity of teaching quality assessments.

Main Methods:

  • Established an index system for teaching quality evaluation.
  • Designed a questionnaire based on the index system to collect data.
  • Developed and optimized a GA-BPNN model for teaching quality assessment.

Main Results:

  • The GA-BPNN algorithm achieved an average evaluation accuracy of 98.56%.
  • This accuracy is 13.23% higher than the standard BPNN model and 5.85% higher than an optimized BPNN model.
  • The GA-BPNN algorithm demonstrated reasonable and scientific results in teaching quality evaluation.

Conclusions:

  • The GA-BPNN algorithm provides a highly accurate and effective method for teaching quality assessment.
  • This optimized algorithm overcomes limitations of traditional evaluation methods.
  • The proposed system offers a scientific and reliable approach to evaluating educational quality.