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Updated: May 23, 2025

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Deep learning based multi attribute evaluation for holistic student assessment in physical education.

Huige Liang1, Huifeng Liang2

  • 1Physical Education, Kyungil University, Gyeongsan-si, Gyeongbuk, 38428, Korea.

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|May 21, 2025
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Summary

This study introduces a deep learning model for comprehensive student assessment in physical education, overcoming limitations of traditional methods. The advanced model offers personalized feedback and objective evaluation, improving educational outcomes.

Keywords:
Deep learningMAEMMulti-attribute evaluation modelPhysical education

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Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education
  • Sports Science Assessment

Background:

  • Traditional physical education assessments are often one-dimensional and lack comprehensiveness.
  • Existing methods struggle to integrate diverse student performance data effectively.
  • There is a need for more objective, scalable, and personalized evaluation tools in physical education.

Purpose of the Study:

  • To propose a deep learning-based multi-attribute user evaluation model for holistic student assessment in physical education.
  • To address the limitations of traditional, one-dimensional assessment approaches.
  • To enhance the accuracy, flexibility, and objectivity of student performance evaluation.

Main Methods:

  • A ten-step methodology involving data collection, preparation, model construction, and deployment.
  • Utilizing deep learning for multi-attribute user evaluation modelling.
  • Integrating diverse data: physical activities, cognitive tasks, emotional responses, and social interactions.

Main Results:

  • The developed model demonstrates high efficacy with improved accuracy and reduced errors.
  • Experimental investigation confirms the model's robustness, indicated by a low mean score.
  • The model provides personalized feedback, enhancing decision-making and educational outcomes.

Conclusions:

  • The deep learning model offers automated, objective, and scalable attributes for superior student assessment in physical education.
  • Visualization tools like heatmaps aid in performance monitoring and adaptive adjustments.
  • This approach effectively tackles multifaceted challenges, enabling targeted interventions for student advancement.