A113: Smart Assessment in University PE Teaching

With the development of educational information and intelligence, the evaluation and improvement of teaching effectiveness in university physical education (PE) have become a research focus. Traditional assessment methods struggle to comprehensively reflect teaching outcomes, while smart technologies offer new approaches for precise evaluation. This study aims to construct a smart technology-based assessment model for university PE teaching effectiveness and explore its practical application to promote innovative development in PE teaching. This study adopts a quasi-experimental design, involving 300 PE teachers and 1500 students from five universities in China. The experimental group used a smart assessment system (including wearable devices and AI classroom analysis tools), while the control group used traditional assessment methods. Data were collected in real-time through smart devices, including student movement data, classroom engagement, and teaching behaviors. Surveys and focus group interviews were conducted to gather feedback from teachers and students. Data analysis employed a mixed-methods approach, with quantitative data analyzed using SPSS for multifactorial ANOVA and qualitative data analyzed using NVivo for thematic analysis. The experimental group significantly outperformed the control group in all dimensions of teaching effectiveness (skill acquisition, classroom engagement, and teaching satisfaction < 0.01). The smart assessment system accurately captured student movement data and classroom performance, providing real-time feedback to teachers. Qualitative analysis revealed high acceptance of smart assessment among teachers and students, who believed it enhanced teaching transparency and personalized guidance. This study is the first to systematically apply smart technologies to assess university PE teaching effectiveness, demonstrating its efficacy. Compared to previous research, this study emphasizes the deep integration of technology and teaching, offering a new paradigm for PE assessment. Future research should focus on optimizing the applicability and cost-effectiveness of smart systems. The practical significance lies in providing an actionable smart assessment framework for university PE, contributing to the overall improvement of teaching quality.