How Drawing Unlocks Students' Developing Mechanistic Reasoning about Complex Systems in Physiology

In science education, student-generated drawings provide valuable insights into students' developing understanding of complex systems. This study examined how undergraduate physiology students' drawings reveal changes in their mechanistic reasoning after experiencing an agent-based modeling environment that modeled a complex system. Students’ drawings at pre- and post-test and speech when learning with the agent-based modeling environment indicate that drawing reveals aspects of students’ knowledge not captured by verbal modes during learning. Specifically, students used the agent-based modeling environment to (1) transform their drawings of the entities' aggregate organization by dynamically integrating prior and newly acquired representational features and (2) identify initially overlooked individual entities and physical properties that play causal roles in the complex system. We illustrate how students’ knowledge manifests dynamically across drawing and speech and discuss the implications of drawing as a key that can unlock the door to productive knowledge resources but also reveal somewhat less productive knowledge in relation to targets for understanding quantified complex systems with multiple causal factors.