Foregrounding motivational climate in university physical education: a mixed-methods latent profile analysis with classroom prescriptions

Background: Evidence on empowering motivational climates in Chinese university physical education is limited, particularly regarding how students cluster into distinct perception patterns and how such heterogeneity can be converted into profile-specific teaching actions that support competence and self-regulation. This study identified latent profiles of university students' physical education perceptions and derived actionable implications aligned with empowering climates. Methods: In a mixed-methods study, undergraduates from 10 universities in Shandong Province (China) completed a survey used to identify latent perception profiles across six learning-related dimensions and to test demographic predictors. Student and teacher interviews were thematically analyzed to interpret profile mechanisms and inform recommendations. Results: Among 1,259 students, three clearly separated profiles emerged (entropy = 0.892). Females had higher odds of membership in lower-support profiles [largest OR = 1.732, 95% CI (1.199, 2.504)]. Second-year students were more likely than first years to belong to the moderate-to-higher profile relative to the low profile [OR = 1.483, 95% CI (1.071, 2.054)]. Interviews suggested that performance visibility and threat constrained engagement, whereas progress referenced assessment with explanatory feedback strengthened perceived competence and self-regulation, helping explain profile differences. Conclusion: Findings position motivational climate as a key lever for improving Chinese university PE and provide a profile-informed implementation pathway. We recommend a classroom prescription combining tiered tasks, structured choice, and low-threat, progress-referenced formative assessment with explanatory feedback. Implementation should prioritize first-year classes and female students, consolidate through self-monitoring and peer assessment, and scale via structured autonomy and resource optimization.