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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.

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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.