Effects of Structural Transparency in System Dynamics Simulators on Performance and Understanding

Abstract

Abstract:Prior exploration is an instructional strategy that has improved performance and understanding in system-dynamics-based simulators, but only to a limited degree. This study investigates whether model transparency, that is, showing users the internal structure of models, can extend the prior exploration strategy and improve learning even more. In an experimental study, participants in a web-based simulation learned about and managed a small developing nation. All participants were provided the prior exploration strategy but only half received prior exploration embedded in a structure-behavior diagram intended to make the underlying model's structure more transparent. Participants provided with the more transparent strategy demonstrated better understanding of the underlying model. Their performance, however, was the equivalent to those in the less transparent condition. Combined with previous studies, our results suggest that while prior exploration is a beneficial strategy for both performance and understanding, making the model structure transparent with structure-behavior diagrams is more limited in its effect.

Optimising the Learning Potential of Simulations Through Structural Transparency and Exploratory Guidance

“…The hypothesis H1 (the level of transparency of the SCC simulation model positively influences the level of comprehension of the model structure) is supported. This finding is consistent with some of the literature on learning from transparent models (Größler et al, 2000; Kopainsky & Alessi, 2015). Students who received a prior explanation of the SFD and were encouraged and supported in using that diagram during the simulation demonstrated, on average, a better understanding of the underlying model structure.…”

“…The students successfully completed all the steps of the structural debriefing but required considerable time to do so. Kopainsky and Alessi (2015) investigated whether model transparency, combined with prior exploration strategy, would improve learning. The authors concluded that participants provided with the more transparent strategy demonstrated better understanding of the underlying model, but their performance was equivalent to those in less transparent conditions.…”

Exploring the Effects of Structural Transparency and Exploratory Guidance in Simulation-Based Learning Environments

“…The hypothesis H1 (the level of transparency of the SCC simulation model positively influences the level of comprehension of the model structure) is supported. This finding is consistent with some of the literature on learning from transparent models (Grobler at al., 2000, Kompainsky andAlessi, 2015). However, the hypothesis H2 (the level of transparency of the SCC simulation model positively influences the level of comprehension of the model behaviour) is not full supported, thus suggesting that by using a transparent simulator solely, students do not learn about the model behaviour any more effectively than they would otherwise.…”

“…Research shows that structural knowledge provided by transparent simulations has the potential to improve leaning and task performance (Größler et al, 2000;Qudrat-Ullah, 2007). Kopainsky and Alessi (2015) found that participants provided with the more transparent strategy demonstrated better understanding of the underlying model, but their performance, however, was the equivalent to those in the less transparent condition. The implication of previous study is that the students must not only identify the structure of the simulator model, but will also have to recognize the relationship between structure and behaviour (Davidsen and Spector, 2015).…”

Teaching Farmers about Fertilization: Ali Saysel's Research on Improving Farmers' Decision‐making

“…Finally, some of our own research suggests that making the simulation model more transparent during game play will improve learning (Kopainsky and Alessi, ) and that allowing reversible (and non‐final) exploration of parameters can improve decision‐making (Kopainsky et al ., ).…”