How drawing unlocks students' developing mechanistic reasoning about complex systems in physiology

Area of Science:

• Physiology education
• Science education research
• Cognitive science

Background:

• Student-generated drawings are valuable tools for assessing understanding of complex systems.
• Mechanistic reasoning is crucial for comprehending physiological processes.
• Agent-based modeling (ABM) offers a dynamic approach to learning complex systems.

Purpose of the Study:

• To investigate how undergraduate physiology students' drawings change after using an agent-based modeling environment.
• To explore how drawing reveals aspects of mechanistic reasoning not evident in verbal explanations.
• To analyze the integration of prior and new knowledge in student representations of complex systems.

Main Methods:

• Comparative analysis of student drawings (pre- and post-intervention).
• Qualitative analysis of student speech during interaction with the ABM environment.
• Examination of representational features and identification of causal roles in drawings.

Main Results:

• Drawings dynamically integrated prior and newly acquired representational features, transforming understanding of aggregate organization.
• Students identified previously overlooked individual entities and physical properties crucial for causal roles.
• Drawing revealed aspects of knowledge not captured by verbal modes during learning.

Conclusions:

• Drawing serves as a critical tool for uncovering students' mechanistic reasoning in complex systems.
• Agent-based modeling environments can facilitate the dynamic integration and refinement of representational knowledge.
• Visual representations, alongside speech, provide a more comprehensive understanding of student learning processes.