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.