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<p>Badminton is one of the most popular student sports, but it is challenging to increase learning efficiency by observing learners using the naked eye without assistive tools. Therefore, this study proposes an auto-feedback badminton teaching app integrated with an auto-feedback-based WISER model. Learners could conduct self-learning with the functions of the badminton teaching app, including automatic grading, automatic feedback, and professional player demonstration videos. This study adopts a quasi-experimental design. The proposed App and model were applied in the experimental group, while the control group used traditional teaching and mobile devices with a video recording function. Both groups of learners learned serve and clear skills for 6 weeks each. The increase in post-test scores of the experimental group was significantly higher than that of the control group, though post-test scores of both groups were significantly higher than pre-test scores. The proposed method demonstrates its efficiency for self-learning, as confirmed by interviews. Future work can apply the integration of motion recognition and the auto-feedback-based WISER model across diverse educational disciplines to personalize students’ learning.</p>

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A study of using mobile phone application in badminton teaching and learning process based on deep learning and auto-feedback-based WISER model

  • 期刊论文

  • 发表日期：

    2025年7月11日

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  • Kuo-Chin Lin,
  • Wen-Chen Cai,
  • Cheng-Wen Ko

摘要

Badminton is one of the most popular student sports, but it is challenging to increase learning efficiency by observing learners using the naked eye without assistive tools. Therefore, this study proposes an auto-feedback badminton teaching app integrated with an auto-feedback-based WISER model. Learners could conduct self-learning with the functions of the badminton teaching app, including automatic grading, automatic feedback, and professional player demonstration videos. This study adopts a quasi-experimental design. The proposed App and model were applied in the experimental group, while the control group used traditional teaching and mobile devices with a video recording function. Both groups of learners learned serve and clear skills for 6 weeks each. The increase in post-test scores of the experimental group was significantly higher than that of the control group, though post-test scores of both groups were significantly higher than pre-test scores. The proposed method demonstrates its efficiency for self-learning, as confirmed by interviews. Future work can apply the integration of motion recognition and the auto-feedback-based WISER model across diverse educational disciplines to personalize students’ learning.