## 4 Discussion  
This study constructed interpretable prediction models based on machine learning techniques to investigate, for the first time, the contributions of multiple digital technology tools to teaching quality in university physical education classes. Simultaneously, SEM was employed to elucidate the underlying mechanisms by which digital technology use, perceived teacher support, and academic self-efficacy influence teaching quality in this context. The findings demonstrate that the RR model delivered superior predictive performance, identifying multimedia courseware, e-textbooks, and question banks as the most influential features in the teaching quality prediction model. The SHAP analysis revealed that management software, websites, and multimedia materials were the core positive predictors, showing significant positive associations with teaching quality. Interestingly, the frequent use of multimedia courseware, question banks, and e-textbooks was found to have a negative impact on teaching quality in physical education settings. This suggests that tools traditionally effective in academic contexts may not translate directly to subjects that emphasize physical interaction and experiential learning. Moreover, an analysis of synergistic effects indicated that combinations of management software, multimedia materials, subject software, and websites yielded significant positive joint effects on teaching quality when used in tandem, underscoring the importance of a strategic integration of complementary digital tools. In addition, the study demonstrated that digital technology use influences teaching quality indirectly through two key mediating pathways: perceived teacher support and academic self-efficacy. These findings validate the chain mediation model, emphasizing that the effectiveness of digital technology in education is not only a function of tool selection, but also of how such tools enhance teacher-student interactions and foster students' confidence in their learning capabilities. By integrating interpretable machine learning models with structural equation modeling, this research provides a robust framework for identifying and understanding the key drivers of teaching quality in digitalized physical education classrooms. The results offer both theoretical insights and practical implications for optimizing the deployment of digital technologies and formulating evidence-based strategies for enhancing instructional effectiveness in higher education. This study is the first to employ interpretable machine learning methods to identify that the use of management software, websites, and multimedia materials in physical education classrooms positively influences teaching quality. Moreover, the combined use of management software, multimedia materials, subject software, and websites produced a stronger synergistic effect in enhancing teaching outcomes. These findings are consistent with previous research showing that integrating diverse digital tools and online resources into classroom instruction fosters more personalized learning experiences and collabora \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Path** & **Effect Value** & **Boot SE** & **Boot LLCI** & **Boot ULCI** & \(p\) \\ \hline Direct effect & Digital technology use \(\rightarrow\) Teaching quality & 0.347 & 0.040 & 0.268 & 0.426 & 0.000 \\ \hline  & Digital technology use \(\rightarrow\) Perceived teacher & 0.303 & 0.035 & 0.238 & 0.374 & 0.000 \\  & support \(\rightarrow\) Teaching quality & & & & & \\ \cline{2-7}  & Digital technology use \(\rightarrow\) Academic & 0.068 & 0.017 & 0.038 & 0.103 & 0.000 \\ \cline{2-7} Indirect effect &
\begin{tabular}{c} Digital technology use \(\rightarrow\) Perceived teacher \\ support \(\rightarrow\) Academic \\ self-efficacy \(\rightarrow\) Teaching quality \\ \end{tabular} & 0.096 & 0.019 & 0.061 & 0.134 & 0.000 \\ \hline Total effect & Digital technology use \(\rightarrow\) Teaching quality & 0.814 & 0.043 & 0.731 & 0.898 & 0.000 \\ \hline \hline \end{tabular}
\end{table}