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
  • [41] A Chat with ChatGPT: The potential impact of Generative AI in higher education learning, teaching and assessment, with specific reference to EAL/D students
    Kebble, Paul
    JOURNAL OF ACADEMIC LANGUAGE AND LEARNING, 2023, 17 (01): : T81 - T91
  • [42] Use of AI-driven Code Generation Models in Teaching and Learning Programming: a Systematic Literature Review
    Cambaz, Doga
    Zhang, Xiaoling
    PROCEEDINGS OF THE 55TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE 2024, VOL. 1, 2024, : 172 - 178
  • [43] Evolution to revolution: Critical exploration of educators' perceptions of the impact of Artificial Intelligence (AI) on the teaching and learning process in the GCC region
    Shamsuddinova, Shakhnoza
    Heryani, Poonam
    Naval, Mark Anthony
    INTERNATIONAL JOURNAL OF EDUCATIONAL RESEARCH, 2024, 125
  • [44] The effect of incorporating large language models into the teaching on critical thinking disposition: An "AI plus Constructivism Learning Theory" attempt
    Wang, Peng
    Yin, Kexin
    Zhang, Mingzhu
    Zheng, Yuanxin
    Zhang, Tong
    Kang, Yanjun
    Feng, Xun
    EDUCATION AND INFORMATION TECHNOLOGIES, 2025,
  • [45] Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis
    Nnadili, Miriam
    Okafor, Andrew N.
    Olayiwola, Teslim
    Akinpelu, David
    Kumar, Revati
    Romagnoli, Jose A.
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2024, 63 (14) : 6313 - 6324
  • [46] A hybrid adaptive teaching-learning-based optimization and differential evolution for parameter identification of photovoltaic models
    Li, Shuijia
    Gong, Wenyin
    Wang, Ling
    Yan, Xuesong
    Hu, Chengyu
    ENERGY CONVERSION AND MANAGEMENT, 2020, 225
  • [47] A hybrid adaptive teaching-learning-based optimization and differential evolution for parameter identification of photovoltaic models
    Li, Shuijia
    Gong, Wenyin
    Wang, Ling
    Yan, Xuesong
    Hu, Chengyu
    ENERGY CONVERSION AND MANAGEMENT, 2020, 225
  • [48] Automated damage detection for open-air and underwater navigation infrastructure using generative AI-produced training data for deep learning models
    Alexander, Quincy G.
    Narazaki, Yasutaka
    Maxwell, Andrew
    Wang, Shengyi
    Spencer, Billie F.
    STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2024,
  • [49] The Evolution of Anticancer 3D In Vitro Models: The Potential Role of Machine Learning and AI in the Next Generation of Animal-Free Experiments
    Momoli, Carolina
    Costa, Beatrice
    Lenti, Lorenzo
    Tubertini, Matilde
    Parenti, Marco Daniele
    Martella, Elisa
    Varchi, Greta
    Ferroni, Claudia
    CANCERS, 2025, 17 (04)
  • [50] TWO-DIMENSIONAL AND THREE-DIMENSIONAL MODELS USED FOR TEACHING HUMAN EVOLUTION IN SECONDARY SCHOOLS. LEARNING PROFICIENCY ASSESSMENT. A CASE STUDY
    Dardon, Ulisses
    Vieira, Viviane
    Serodio Prestes, Stella Barbara
    Cunha Parmera, Thais de Castro
    Cotts, Leonardo
    Bergqvist, Lilian Paglarelli
    JOURNAL OF SEDIMENTARY ENVIRONMENTS, 2016, 1 (02): : 279 - 291
12345