Educational environment perceptions across curriculum formats: structural associations among DREEM domains and...

Team-Based Learning (TBL) is increasingly used in health professions education; however, the Dundee Ready Education Environment Measure (DREEM) has rarely been formally evaluated in TBL-based medical curricula. This study examined the structural validity of the DREEM, assessed whether associations with academic self-perceptions differed by year and gender, tested an indirect pathway between learning quality and atmosphere, and evaluated group mean differences.

A cross-sectional survey was administered to 411 Year 1 and Year 2 medical students at the Alfaisal University College of Medicine, Riyadh, Saudi Arabia. Analyses included confirmatory factor analysis using weighted least squares mean and variance adjusted estimation, multigroup structural equation modeling, bootstrapped mediation with 5,000 simulations, and multivariate analysis of variance (MANOVA).

Confirmatory factor analysis showed mixed but overall supportive evidence for the five-factor structure (comparative fit index = .996, Tucker-Lewis index = .996, standardized root mean square residual = .086; root mean square error of approximation = .103). The internal consistency coefficients ranged from .643 to .942. Among Year 1 students, academic self-concept was associated with learning and social perceptions, whereas among Year 2 students, all four environmental domains were associated with academic self-concept. Among male students, teacher and social perceptions were more strongly associated with academic self-concept, whereas among female students, learning quality and atmosphere were more strongly associated. Academic self-perceptions showed an indirect association consistent with the partial mediation of the learning-atmosphere relationship. No significant multivariate differences emerged between academic years or genders.

For a given institution, these findings support complementing aggregate DREEM scores with subscale and structural analyses to identify cohort-specific patterns across its TBL and TBL + PBL preclinical learning environments.