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Optimization Model of Mathematics Instructional Mode Based on Deep Learning Algorithm.
1Science Teaching Department, Zhengzhou Preschool Education College, Zhengzhou 450000, China.
Computational Intelligence and Neuroscience
|July 14, 2022
Summary
This study introduces a novel teaching evaluation model using deep learning (DL) algorithms to assess mathematics instruction quality. The model achieves high stability and accuracy, offering objective insights for improving teaching methods.
Area of Science:
- Educational Technology
- Artificial Intelligence in Education
- Mathematics Education
Background:
- Current mathematics teaching methods require objective evaluation.
- Existing instructional quality assessments may lack scientific rigor.
- Deep learning (DL) offers potential for advanced educational analysis.
Purpose of the Study:
- To develop and validate a DL-based model for evaluating mathematics instructional quality.
- To identify specific areas for improvement in mathematics teaching practices.
- To establish a fair and rational index system for instructional quality assessment.
Main Methods:
- Literature review on mathematics instructional modes and DL theories.
- Construction of a teaching evaluation model utilizing DL algorithms.
- Development of an instructional quality index system.
- Training and testing using a Backpropagation Neural Network (BPNN) model on instructional quality data.
Main Results:
- The developed system demonstrates high stability (96.37%) and evaluation accuracy (95.42%).
- The model provides objective and reasonable assessments of mathematical instructional quality.
- The system effectively identifies weaknesses in the teaching process.
Conclusions:
- The DL-based evaluation model offers a workable system for assessing instructional quality in mathematics.
- The model can provide data-driven recommendations for enhancing teaching effectiveness.
- This approach contributes to the scientific analysis of educational practices.
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