A Comparative Causal Model of Academic Self-Regulation: Examining Motivational, Emotional, and Cognitive Pathways in Blended and Face-to-Face Medical Graduate Education (Preprint)

BACKGROUND Medical graduate education increasingly uses blended and online delivery, although students' academic self-regulation may be shaped by different motivational and cognitive processes across learning contexts, with emotional factors potentially playing a complementary role. Understanding how these mechanisms operate and whether their structural relationships differ between online/blended and face-to-face formats can inform targeted educational supports. OBJECTIVE The present investigation developed and tested a comparative causal model of academic self-regulation among medical graduate students in online/blended versus face-to-face programs. We examined how key motivational constructs (eg, academic self-efficacy, task value, future orientation, perfectionism, and academic help-seeking), positive achievement emotions, and cognitive factors (cognitive academic engagement and need for closure) relate to academic self-regulation, and whether these relationships differ by learning context. METHODS The design was cross-sectional, comparative causal modeling. Participants were master’s-level students at Shahid Beheshti University of Medical Sciences enrolled in either face-to-face (population n=1554; sample n=310) or blended/online (population n=449; sample n=205) programs selected using cluster sampling. Data were collected using validated instruments measuring academic self-regulation (Bouffard scale), academic self-efficacy (Midgley et al), academic engagement (Schaufeli & Bakker), multidimensional perfectionism (Frost), academic help-seeking (Ryan & Pintrich), task value (Pintrich), future orientation (Seginer), need for closure (DeBacker & Crowson), and achievement emotions (AEQ; Pekrun et al). Data were analyzed using path analysis/structural equation modeling. Model fit was evaluated using χ²/df, CFI, GFI, AGFI, and RMSEA. Direct, indirect, and total effects were estimated for each group, and comparative interpretation focused on effect patterns and explained variance. RESULTS The hypothesized causal model reached an acceptable fit in both face-to-face and blended/online groups (χ²/df approximately