Advances in Physiology Education: My Journal and Yours Too?

The optimization of college tennis training and teaching under deep learning

To enhance the integration of deep learning into tennis education and instigate reforms in sports programs, this paper employs deep learning techniques to analyze tennis tactics. The experiments initially introduce the concepts of sports science and backpropagation neural networks. Subsequently, these theories are applied to formulate a comprehensive system of tennis tactical diagnostic indicators, encompassing construction principles, basic requirements, diagnostic indicator content, and evaluation indicator design. Simultaneously, a Back Propagation Neural Network (BPNN) is utilized to construct a tennis tactical diagnostic model. The paper concludes with a series of experiments conducted to validate the effectiveness of the constructed indicator system and diagnostic model. The results indicate the excellent performance of the neural network model when trained on tennis match data, with a mean squared error of 0.00037146 on the validation set and 0.0104 on the training set. This demonstrates the outstanding predictive capability of the model. Additionally, the system proves capable of providing detailed tactical application analysis when employing the tennis tactical diagnostic indicator system for real-time athlete diagnosis. This functionality offers robust support for effective training and coaching during matches. In summary, this paper aims to evaluate athletes' performance by constructing a diagnostic system, providing a solid reference for optimizing tennis training and education. The insights offered by this paper have the potential to drive reforms in sports programs, particularly in the realm of tennis education.

TESOT: a teaching modality targeting the learning obstacles in global medical education

In higher education, it is a great challenge for instructors to teach international medical students (IMSs) efficiently. These students usually have different learning obstacles and learning style preferences from domestic students. Thus it is necessary to use teaching modalities targeting the specific characteristics of IMSs. Accordingly, we have developed a teaching modality composed of classical teacher-centered approach (TCA), enriched with components of student-centered approach (SCA) and online interactions targeting the learning characteristics of IMSs, which we defined as TESOT (an acronym made of the underlined words' initials). Aside from the online interactions that provide both answers to questions raised by students and guidance throughout a course, this modality contains additional in-classroom components (i.e., pre-lecture quiz, student-led summary, and post-lecture quiz). The effectiveness of this modality was tested in the nervous system module of the Physiology course for IMSs. The final exam scores in the nervous system module in the year taught with TESOT were higher than those earned by students taught with a classical TCA modality in preceding 2 yr. The improvement of teaching effectiveness is attributable to increasing communication, bridging course contexts, and meeting diverse learning style preferences. These results indicate that TESOT as an effective teaching modality is useful for enhancing efficiency of teaching IMSs.