## 5 Conclusions  
This study is the first to integrate interpretable machine learning methods with structural equation modeling to systematically examine the contributions and underlying mechanisms of digital technology use on teaching quality in university-level physical education. The findings indicate that management software, websites, and multimedia materials serve as key positive predictors of teaching quality in this context. In contrast, the frequent use of multimedia courseware, question banks, and e-textbooks was associated with negative effects on teaching quality. Additionally, significant positive synergistic effects emerged when management software, multimedia materials, subject software, and websites were used in combination, underscoring the importance of strategically integrating complementary digital tools. The study further demonstrated that digital technology use influences teaching quality not only directly but also indirectly through the mediating roles of perceived teacher support and academic self-efficacy. By shifting the research focus beyond traditional academic disciplines, this study addresses a critical gap in the literature and offers new empirical evidence on the function of digital tools within the distinct pedagogical context of physical education. In doing so, it provides both theoretical insights and practical recommendations for optimizing digital technology integration in physical education, thereby contributing to the advancement of evidence-based digital pedagogy in higher education. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15147689/s1](https://www.mdpi.com/article/10.3390/app15147689/s1)]([https://www.mdpi.com/article/10.3390/app15147689/s1](https://www.mdpi.com/article/10.3390/app15147689/s1)). Figure S1: Lasso regression analysis. (A) Coefficient path plot, (B) Cross-validation error curve for regularization parameter (\(\lambda\)). Figure S2: Learning curve of the random forest classifier for teaching quality prediction. Figure S3: Comparison of ridge regression-based confusion matrices for quality of teaching across digital technology features. Figure S4: Predicted teaching quality distribution across usage frequencies of digital technology tools. (A) Question bank, (B) Subject software, and (C) E-textbooks. Figure S5: Contour maps of synergistic effects on predicted teaching quality across pairs of digital technology tools. Table S1: Details of hyperparameter for ML models. Table S2. Assessment of multicollinearity among model variables using variance inflation factors (VIF). Conceptualization, L.Z. (Liguo Zhang); methodology, L.Z. ([PERSON]); software, L.Z. ([PERSON]); validation, Z.L. ; formal analysis, L.Z. (Liguo Zhang); investigation, L.Z. ([PERSON]); resources, L.Z. ([PERSON]); data curation, L.Z. ([PERSON]); writing--original draft preparation, L.Z. ([PERSON]); writing--review and editing, L.Z. ([PERSON]) and L.Z. ([PERSON]); visualization, [PERSON]; supervision, L.Z. ([PERSON]); project administration, L.Z. ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by the Shandong Humanities and Social Sciences Project, grant number 20 CLYJ34. This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the School of Basic Medical Sciences, Shandong University (protocol code 2021-1-114). Informed Consent Statement: Informed consent was obtained from all subjects involved in this study. The original contributions presented in this study are included in this article; further inquiries can be directed to the corresponding author. We sincerely thank all participants, contributors, and supporters who took part in this questionnaire survey. We also greatly appreciate the feedback and suggestions provided by the journal editors and reviewers. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: