Based on Optimization Research on the Evaluation System of English Teaching Quality Based on GA-BPNN Algorithm
Yafei Chen1, Zhenbang Yu1, Weihong Zhao1
1Qingdao Huanghai University, Qingdao, Shandong 266555, China.
Computational Intelligence and Neuroscience
|January 17, 2022
Summary
This study introduces an optimized GA-BP neural network algorithm for evaluating English teaching quality. The new method improves accuracy and stability over traditional approaches, offering a more scientific evaluation system.
Area of Science:
- Educational Technology
- Artificial Intelligence
- Machine Learning
Background:
- English teaching quality is crucial for foundational education and student learning.
- Traditional evaluation methods suffer from subjectivity, incomplete indices, and distorted results.
- Developing a robust English teaching quality evaluation system is a key research focus.
Purpose of the Study:
- To address limitations in traditional English teaching quality evaluation.
- To develop and optimize an evaluation system using an advanced algorithm.
- To enhance the scientific validity and applicability of teaching quality assessments.
Main Methods:
- Utilized a Backpropagation (BP) neural network algorithm for initial evaluation simulation.
- Integrated a Genetic Algorithm (GA) to optimize the BP neural network.
- Further refined the GA-BP model using GA adaptive degree variation and entropy methods.
Main Results:
- The optimized GA-BP neural network algorithm demonstrated faster convergence and reduced error rates.
- The evaluation model exhibited enhanced adaptability and stability compared to conventional methods.
- Achieved evaluation results that more closely align with ideal values, indicating higher scientific accuracy.
Conclusions:
- The optimized GA-BP neural network algorithm provides a more scientific and reliable method for evaluating English teaching quality.
- This advanced approach offers significant improvements in accuracy, stability, and applicability for educational assessments.
- The findings support the adoption of this optimized algorithm for more effective English teaching quality evaluation systems.
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