Abstract for: Effects of Illustrations, Specific Contexts, and Instructions: Further Attempts to Improve Stock–Flow Task Performance
Although we face a multitude of complex dynamic systems every day, there is empirical evidence that even simple ones such as stock–flow (SF) systems are extremely difficult to understand. Based on different theoretical approaches and on previous findings in educational and cognitive research, the current study investigated two approaches to improving performance in SF tasks: invoking valid mental models and building new suitable mental models. In two experiments, the effects of net-flow data illustrations, supportive chart representations, selected contextual scenarios, and two adapted educational methods (informative instruction and induced discovery) on SF task performance were empirically tested. Results indicate that none of the approaches led to increased SF task performance. However, gender and mathematical skills were found to be valid predictors of task solutions.