Predicting College Student Engagement in Physical Education Classes Using Machine Learning and Structural Equation Modeling

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.