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Track and Field Teaching Based on Computer Network Resources.

  • Published In: International Journal of High Speed Electronics & Systems, 2025, v. 34, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Ma, Fuxing 3 of 3

Abstract

Track and field teaching has always been an important part in school physical education (PE). With the deepening of curriculum reform and the continuous growth of IT, universities have gradually broken the old instructional mode, and have set up online teaching platforms and developed new instructional modes. How to integrate modern teaching and learning theory into the new teaching technology platform is the requirement of the times and the inevitable theme of the current PE reform. In this article, the track and field instructional resources under the platform of instructional resources management are studied, and the classification mining algorithm is used to mine and analyze the students' interest data, so as to find out the rules and patterns of users' access to instructional resources, thus further optimizing the allocation of users' access to instructional resources, improving the efficiency of users' access to instructional resources and the utilization rate of instructional resources. Experiments show that the improved collaborative filtering (CF) algorithm based on deep learning is superior to the other two algorithms in recommendation error, and the error is reduced by 10.69% compared with the traditional CF algorithm. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of High Speed Electronics & Systems. 2025/06, Vol. 34, Issue 2, p1
  • Document Type:Article
  • Subject Area:Sports and Leisure
  • Publication Date:2025
  • ISSN:0129-1564
  • DOI:10.1142/S0129156424400305
  • Accession Number:184999733
  • Copyright Statement:Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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