Visualization of Basketball Tactical Evolution in Generative AI Big Models for Teaching and Learning

被引：0
|
作者：
Liu, Zhuoxiao [1 ]
机构：
[1] School of Physical Education, Shanghai University of Sport, Shanghai,200438, China
来源：
Applied Mathematics and Nonlinear Sciences | 2024年 / 9卷 / 01期
关键词：
Basketball tactics teaching occupies a major position in both professional sports teams and schools; even in social basketball training institutions. The paper suggests an improvement plan based on the shortcomings of the generative AI grand model; and integrates the enhanced model into the basketball tactics teaching process to create a visualization method for basketball tactics evolution. The proposed teaching model was applied to students' basketball tactics training courses; and the use of the teaching experiment was investigated by using comprehensive research methods such as questionnaire survey method; paired-sample t-test; and one-way ANOVA; with students majoring in physical education in GT College as the experimental subjects. The results showed that the individual offensive and defensive tactics; local offensive and defensive tactics; and team-wide offensive and defensive tactics of the students in the experimental group and the control group were significantly improved after the experiment relative to the pre-experimental period and that the improvement of the experimental group (9.655-13.989) was more significant than that of the control group (4.844-6.515); and that the experimental group assisted with the basketball tactics teaching model based on the generative AI was more effective than the control group assisted with traditional tactics training method. Control the group using the conventional tactical training method. The study provides a reference for the implementation of teaching reform and improvement of teaching quality in the process of teaching basketball tactics in the future. © 2024 Zhuoxiao Liu; published by Sciendo;
D O I：
10.2478/amns-2024-1915
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [31] Enhancing interpretability and bias control in deep learning models for medical image analysis using generative AI
    Minutti-Martinez, Carlos
    Escalante-Ramirez, Boris
    Olveres, Jimena
    OPTICS, PHOTONICS, AND DIGITAL TECHNOLOGIES FOR IMAGING APPLICATIONS VIII, 2024, 12998
  • [32] Unveiling the evolution of generative AI (GAI): a comprehensive and investigative analysis toward LLM models (2021–2024) and beyond
    Zarif Bin Akhtar
    Journal of Electrical Systems and Information Technology, 11 (1)
  • [33] An Edge-Cloud Collaboration Framework for Generative AI Service Provision With Synergetic Big Cloud Model and Small Edge Models
    Tian, Yuqing
    Zhang, Zhaoyang
    Yang, Yuzhi
    Chen, Zirui
    Yang, Zhaohui
    Jin, Richeng
    Quek, Tony Q. S.
    Wong, Kai-Kit
    IEEE NETWORK, 2024, 38 (05): : 37 - 46
  • [34] Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models
    Alwahedi F.
    Aldhaheri A.
    Ferrag M.A.
    Battah A.
    Tihanyi N.
    Internet of Things and Cyber-Physical Systems, 2024, 4 : 167 - 185
  • [35] Learning to Make Rare and Complex Diagnoses With Generative AI Assistance: Qualitative Study of Popular Large Language Models
    Abdullahi, Tassallah
    Singh, Ritambhara
    Eickhoff, Carsten
    JMIR MEDICAL EDUCATION, 2024, 10
  • [36] Correction: Unveiling the evolution of generative AI (GAI): a comprehensive and investigative analysis toward LLM models (2021–2024) and beyond
    Zarif Bin Akhtar
    Journal of Electrical Systems and Information Technology, 12 (1)
  • [37] Correction: Unveiling the evolution of generative AI (GAI): a comprehensive and investigative analysis toward LLM models (2021–2024) and beyond
    Zarif Bin Akhtar
    Journal of Electrical Systems and Information Technology, 11 (1)
  • [38] The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers?
    Chan, Cecilia Ka Yuk
    Lee, Katherine K. W.
    SMART LEARNING ENVIRONMENTS, 2023, 10 (01)
  • [39] The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers?
    Cecilia Ka Yuk Chan
    Katherine K. W. Lee
    Smart Learning Environments, 10
  • [40] Using a Variety of Interactive Learning Methods to Improve Learning Effectiveness: Insights from AI Models Based on Teaching Surveys
    Barnett-Itzhaki, Zohar
    Beimel, Dizza
    Tsoury, Arava
    ONLINE LEARNING, 2023, 27 (03): : 363 - 386
12345