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Decision Tree Algorithm-Based Model and Computer Simulation for Evaluating the Effectiveness of Physical Education in Universities

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  • Zhifei Zhang
  • Zijian Zhao
  • Doo-Seoung Yeom

Abstract

In this paper, the forest algorithm and the decision tree algorithm are mainly used to analyze students’ physical education information, course exam results, and student learning data and relevant feature attributes from the online teaching platform. We aim to generate decision trees using the decision tree algorithm for the purpose of generating classification rules, based on which we can find factors that are important to students’ physical education performance and form data basis for improving teaching quality to help teaching management and teachers improve teaching methods and adjust teaching strategies. We specifically achieved this objective by constructing a model for assessing the effectiveness of student teaching, the steps of which include data collection and preparation, data preprocessing (data cleaning, conversion, integration), model construction (algorithm training), and algorithm optimization, as well as realizing the simulation results of the model. At the same time, the importance of the relevant attributes of the model is analyzed, and some measures are proposed to improve the universities: the standard of physical education teaching and the corresponding strategies for improving teaching methods. The mainstream development environment is chosen to ensure the complete operation of the project system that integrates learning, operation, and evaluation. The sports virtual simulation experimental teaching system realized in this paper has good functionality, stability, and application benefits in operation and use.

Suggested Citation

  • Zhifei Zhang & Zijian Zhao & Doo-Seoung Yeom, 2020. "Decision Tree Algorithm-Based Model and Computer Simulation for Evaluating the Effectiveness of Physical Education in Universities," Complexity, Hindawi, vol. 2020, pages 1-11, October.
  • Handle: RePEc:hin:complx:8868793
    DOI: 10.1155/2020/8868793
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    Cited by:

    1. Kelum Sandamal & Sachini Shashiprabha & Nitin Muttil & Upaka Rathnayake, 2023. "Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    2. Dania Ortiz & Vera Migueis & Vitor Leal & Janelle Knox-Hayes & Jungwoo Chun, 2022. "Analysis of Renewable Energy Policies through Decision Trees," Sustainability, MDPI, vol. 14(13), pages 1-31, June.

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