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Evaluation of physical education teaching effect using Random Forest model under artificial intelligence.

Xiaowei Jiang1, Yuwei Du2, Yingying Zheng3

  • 1College of Physical Education, Chengdu University, Chengdu, 610106, China.

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|January 3, 2024
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Summary

This study introduces a novel deep learning algorithm to enhance physical education (PE) teaching. The optimized Genetic Algorithm-Back Propagation-Random Forest (GA-BP-RF) model significantly improves teaching accuracy and efficiency for college students.

Keywords:
Artificial intelligenceBig dataNeural networkPE teaching effect evaluationRandom Forest model

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Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education
  • Machine Learning Applications

Background:

  • Current physical education (PE) teaching methods face challenges in effectively cultivating high-level college students.
  • Deficiencies in college teachers' instructional abilities hinder optimal PE outcomes.
  • Existing algorithms like Random Forest (RF) have limitations in node splitting, impacting model performance.

Purpose of the Study:

  • To optimize the physical education (PE) teaching effect using deep learning (DL).
  • To develop an improved algorithm addressing limitations in node splitting for enhanced teaching effectiveness.
  • To apply advanced DL techniques to cultivate higher-level college students in PE.

Main Methods:

  • A novel optimization algorithm was developed to improve node splitting in the Random Forest (RF) algorithm.
  • The algorithm recombines Iterative Dichotomiser 3 and Classification and Regression Tree methods with adaptive parameter selection.
  • The proposed Genetic Algorithm-Back Propagation-Random Forest (GA-BP-RF) model was trained and tested.

Main Results:

  • The network loss function demonstrated a stable downward trend during training, indicating model convergence.
  • The GA-BP-RF algorithm achieved high accuracy (over 95%) with low time consumption (under 5.4 ms).
  • Performance metrics showed significant improvements compared to unoptimized Genetic Algorithm (GA) and Genetic Algorithm-Back Propagation (GA-BP) models.

Conclusions:

  • The proposed GA-BP-RF algorithm is a feasible approach to enhance PE teaching effectiveness.
  • Deep learning technology offers a promising avenue for improving teachers' instructional abilities in PE.
  • The study provides a valuable model for applying DL to educational settings, particularly in PE.