Effects of Instructional Guidance on Modeling Causal Loop Diagrams in Climate Change Education

Climate change education requires learners to understand complex systems and dynamic interdependencies, making systems modeling an important competence within education for sustainable development. This study investigated how instructional guidance influences students’ modeling of causal loop diagrams when working with the climate simulation En-ROADS. In a two-phase design, 242 secondary school students first explored En-ROADS and constructed an initial diagram representing their intermediate knowledge, then refined it while receiving conceptual guidance, procedural guidance, both, or none. Structural complexity of the causal loop diagrams and cognitive load were assessed after each phase. Structural complexity increased significantly in all four conditions, η2p = 0.22. Procedural guidance was associated with a smaller increase, η2p = 0.02, located primarily in the branching structures of the diagrams, and with higher germane cognitive load, η2p = 0.02. Conceptual guidance showed no overall effect but interacted with intermediate knowledge, β = 0.23; as the conditional effects were not significant, this indicates that its influence differed across levels of intermediate knowledge rather than that it benefited either group. The structural complexity of the initial diagram was by far the strongest predictor of that of the refined one, underlining the importance of aligning instructional guidance with learners’ intermediate knowledge in complex digital modeling environments.