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

Xiaoyu Tang1, Matthew Lira1

  • 1Learning Sciences & Educational Psychology, Psychological and Quantitative Foundations, University of Iowa, Iowa City, Iowa, United States.

Advances in Physiology Education
|July 14, 2026
PubMed
Summary

Student drawings offer unique insights into understanding complex systems in science education. Agent-based modeling helps students refine their mechanistic reasoning, revealing knowledge not always captured by verbal explanations.

Keywords:
complex systemsdrawingknowledge in piecesmechanistic reasoningphysiology education

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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.