This article deals with learning from the exploration of system dynamics models. System dynamics modeling intends to improve judgment and decision, but is very time consuming. Model-based interactive learning environments allow saving time, but critics doubt the effectiveness for deep learning. The question is if there is a third way in-between. Relevant examples from system dynamics are analyzed to identify the key activities that trigger learning; they are organized as a structured exploration process, making learners ask relevant questions, obtain valid responses and correctly interpret them. Based upon this, a process for guided rediscovery is proposed together with guidelines for the functional properties of a “systemic exploratory”. Guided rediscovery enables non-specialists to gain relevant insights into dynamically complex situations and is a tool for decision policy design.
"Learning from rediscovering system dynamics models,"
Systèmes d'Information et Management: Vol. 14:
4, Article 6.
Available at: https://aisel.aisnet.org/sim/vol14/iss4/6