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Personalised Recommendation of PE Network Course Environment Resources Using Data Mining Analysis.

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This study introduces a data mining (DM) system to enhance physical education (PE) by recommending relevant course resources. The system achieves high accuracy, improving PE curriculum resource recommendations.

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Area of Science:

  • Educational Technology
  • Data Mining
  • Curriculum Development

Background:

  • Physical Education (PE) reform benefits from supplementary digital resources.
  • Traditional teaching methods can be enhanced by network-based curriculum systems.
  • Vast amounts of data in online PE resources require advanced analysis.

Purpose of the Study:

  • To develop a data mining (DM) based recommendation system for physical education (PE) course resources.
  • To improve the accuracy and effectiveness of personalized curriculum resource suggestions.
  • To support PE education reform through intelligent resource allocation.

Main Methods:

  • Utilized highly automated data mining (DM) technology to analyze network PE curriculum data.
  • Developed a recommendation system incorporating user registration, course retrieval, browsing history, and scoring.
  • Implemented algorithms to forecast user actions and suggest relevant course materials based on preferences.

Main Results:

  • The recommendation system demonstrated a recommendation accuracy of up to 96.2%.
  • Achieved approximately 10% higher accuracy compared to conventional recommendation methods.
  • Showcased good recommendation effect, high accuracy, and coverage for personalized learning.

Conclusions:

  • The developed system offers an effective solution for individualized PE curriculum resource recommendations.
  • The data mining approach enhances the accuracy of hidden semantic models in educational contexts.
  • This system positively influences PE education reform by optimizing resource utilization.