The Interplay of Self-regulation, Cognitive Load and Learner Resources: Implications for Learning Outcomes in Virtual Reality

This study investigated how cognitive load, self-regulation, and learner resources interact to shape conceptual learning in immersive virtual reality (iVR). Building on Seufert’s (2018) model, we examined whether its predictions hold in iVR, how intrinsic (ICL) and extraneous cognitive load (ECL) relate to self-regulation and learning, and how learner resources contribute to these relations. Data from 473 students showed a consistent negative linear effect of ECL on both self-regulation and learning, underscoring ECL as a critical burden in iVR-based learning. ICL did not predict the frequency of self-regulation but negatively moderated its effectiveness for learning. In terms of learner resources, interest and self-efficacy supported the application of self-regulation under load, whereas prior knowledge was more closely related to whether regulatory activity translated into learning. These findings diverge from the U-shaped relations proposed by Seufert (2018) and suggest that in iVR, established relations between cognitive load and self-regulation are overlaid by additional regulatory demands inherent to immersive environments. Consequently, optimizing iVR-based learning requires not only minimizing ECL but also explicitly supporting learning-relevant self-regulation. Overall, the results demonstrate that models of self-regulated learning validated in traditional settings cannot be assumed to transfer unaltered to immersive environments, with implications for both instructional design and theory-driven research on learning in iVR. • Extraneous load consistently undermines learning and self-regulation in iVR. • Intrinsic load constrains the effectiveness, not the frequency, of self-regulation. • Motivational learner resources support self-regulation under cognitive load. • Self-regulation may be diverted from learning to managing the iVR environment.