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Conceptual understanding and cognitive patterns construction for physical education teaching based on deep learning

Long Zhao1, Guoping Wu2, Weining Shao3

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|December 29, 2024
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Summary
This summary is machine-generated.

This study introduces a deep learning method to enhance physical education (PE) teaching concept understanding and analyze student cognitive patterns. The proposed models significantly improve concept mastery and provide accurate cognitive state predictions for personalized learning support.

Keywords:
Association learningCognitive patternsConceptual understandingDeep learningHypergraphic convolutionPhysical education

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Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education
  • Cognitive Science

Background:

  • Improving student comprehension of physical education (PE) concepts is crucial.
  • Teachers require tools to analyze student cognitive patterns effectively.
  • Deep learning offers potential for analyzing complex learning data.

Purpose of the Study:

  • To develop an association learning-based method for understanding PE teaching concepts.
  • To construct a neurocognitive diagnostic model for identifying student cognitive outcomes.
  • To enhance instructional guidance and personalized learning through cognitive pattern analysis.

Main Methods:

  • Utilized deep learning algorithms, specifically convolutional neural networks (CNNs), for extracting image features related to PE teaching concepts.
  • Developed a neurocognitive diagnostic model employing hypergraph convolution to analyze long-term student learning sequences.
  • Trained and validated models using extensive datasets to assess accuracy and predictive capabilities.

Main Results:

  • The association graph convolutional neural network achieved a highest accuracy of 0.84 with 90,000 training samples.
  • The cognitive diagnostic model demonstrated accuracies of 0.76, 0.77, and 0.75 across three datasets.
  • Student mastery of sports concepts and knowledge increased by 29% using the association graph convolutional neural network.
  • The cognitive schema diagnostic model showed a mean predictive accuracy of 0.81 (ranging from 0.6 to 1.0).

Conclusions:

  • The proposed models exhibit high accuracy and stability in predicting cognitive patterns.
  • The developed method effectively identifies students' cognitive states, supporting instructional guidance.
  • This approach provides a strong foundation for personalized learning experiences in physical education.