Evaluation Method of Physical Education Teaching and Training Quality Based on Deep Learning

Area of Science:

• Educational Technology
• Artificial Intelligence in Education
• Sports Science

Background:

• Traditional physical education (PE) teaching evaluation methods face challenges with accuracy and complexity.
• There is a need for more reliable and objective assessment tools in PE.

Purpose of the Study:

• To propose a novel deep learning-based evaluation method for physical education teaching and training quality.
• To overcome the limitations of existing evaluation techniques in PE.

Main Methods:

• Constructed an evaluation index system encompassing teaching content, attitude, methods, and effects.
• Quantified influence factors, dynamically applied resolution coefficients, and established a weighted index correlation.
• Utilized score distribution and progress as key evaluation metrics.

Main Results:

• The deep learning model achieved a high correlation coefficient of 0.9430 with actual results.
• The proposed evaluation method demonstrated an accuracy rate of 94.73% in tests.
• The system effectively addresses the difficulties and inaccuracies in traditional PE evaluation.

Conclusions:

• Deep learning offers a powerful solution for accurate and efficient physical education teaching quality assessment.
• The developed method provides a reliable framework for evaluating PE outcomes.
• This approach can significantly enhance the objectivity and effectiveness of PE teacher training and evaluation.