Zetan Liu

Amid the ongoing digital transformation of higher education, increasing attention has been paid to the impact of digital technologies on teaching quality—particularly in physical education settings that require high levels of interaction and physical engagement. This study examined 1158 university students across China using a cross-sectional design, integrating interpretable machine learning models with structural equation modeling to systematically assess how the frequency of use of seven common digital technologies influences teaching quality in physical education classes. The study also investigated the mediating roles of perceived teacher support and academic self-efficacy. Nine machine learning models and logistic regression analyses were conducted to predict teaching quality, employing feature permutation importance and SHapley Additive exPlanations (SHAP) to evaluate the relative contribution of each digital tool. Results identified multimedia courseware, e-textbooks, and question banks as the most influential predictors of teaching quality. The SHAP analysis further revealed that management software, educational websites, and multimedia materials had significant positive effects and exhibited synergistic interactions in improving teaching outcomes. Structural equation modeling showed that digital technology use had a significant direct positive effect on teaching quality (B = 0.347, p < 0.001). Moreover, perceived teacher support and academic self-efficacy not only served as independent mediators (95% CI = [0.146, 0.226], p < 0.001; 95% CI = [0.024, 0.063], p < 0.001) but also functioned as a chain mediating effect (95% CI = [0.036, 0.083], p < 0.001). This study is the first to integrate interpretable machine learning with structural modeling to elucidate the mechanisms through which digital technologies influence teaching quality in university-level physical education. The findings underscore the critical mediating roles of teacher support and student self-efficacy, providing both theoretical contributions and practical implications for enhancing digital pedagogy in higher education.

Digital technology has become increasingly prevalent in higher education classrooms. However, the impact of different types and use frequencies of digital technology on college students’ classroom engagement can vary substantially. This study aims to develop an interpretable machine learning model to predict student classroom engagement based on various digital technologies and construct a structural equation model (SEM) to further investigate the underlying mechanisms involving perceived usefulness (PU), perceived ease of use (PEU), and academic self-efficacy (ASE). Nine machine learning algorithms were employed to develop interpretable predictive models, rank the importance of digital technology tools, and identify the optimal predictive model for student engagement. A total of 1158 eligible Chinese university students participated in this study. The results indicated that subject-specific software, management software, websites, and mobile devices were identified as key factors influencing student engagement. Interaction effect analyses revealed significant synergistic effects between management software and subject-specific software, identifying them as primary determinants of student engagement. SEM results demonstrated that digital technology usage frequency indirectly influenced student engagement through PU, PEU, and ASE, with both PU and ASE as well as PEU and ASE playing chain-mediated roles. The findings underscore the importance of integrating digital tools strategically in PE classrooms to enhance engagement. These insights offer practical implications for higher education institutions and policymakers.