Fish Swim, Rocks Sit, and Lungs Breathe: Expert-Novice Understanding of Complex Systems

• Published in: The Journal of the learning sciences 2007-01, Vol.16 (3), p.307-331
• Main Authors: Hmelo-Silver, Cindy E., Marathe, Surabhi, Liu, Lei
• Format: Article
• Language: English
• Subjects: Anatomy; Biology; Cognitive models; Complex systems; Complexity Strand Article; Convergent Thinking; Delphi Technique; Ecology; Ecosystems; Entry Workers; Individual Differences; Interrater Reliability; Knowledge Representation; Learning; Lungs; Matched Groups; Oxygen; Physiology; Preservice teachers; Public aquariums; Respiratory system; Specialists; Systems Analysis; Systems Approach
• ISSN: 1050-8406; 1532-7809
• DOI: 10.1080/10508400701413401

Understanding complex systems is fundamental to understanding science. The complexity of such systems makes them very difficult to understand because they are composed of multiple interrelated levels that interact in dynamic ways. The goal of this study was to understand how experts and novices differed in their understanding of two complex systems, the human respiratory system and an aquarium ecosystem. In particular, we examined how a representation of complex systems, Structure-Behavior-Function theory (SBF), might account for these differences. SBF is particularly relevant in understanding biological systems because an important domain principle is the relation between form, function, and mechanism. Our results demonstrated that there were minimal differences between the expert and novice groups on structures, but that the locus of the difference was on understanding causal behaviors and functions, the least salient elements of the systems. Mental model analysis provided largely convergent results. We also found differences between the two different kinds of experts in each domain. These results suggest that SBF does capture expert-novice differences and may have implications for instruction. This research was funded by National Science Foundation CAREER Grant 0133533 to Cindy E. Hmelo-Silver. Conclusions or recommendations expressed in this material are our own and do not necessarily reflect the views of the National Science Foundation. We thank Vera Tuchapsky for her assistance with coding the data and Rebecca Jordan for providing feedback on an earlier draft. We thank Paul Feltovich and two anonymous reviewers for their helpful feedback. Portions of this research have been presented at the annual meeting of the European Association for Research on Learning and Instruction (2003), the annual convention of the American Psychological Association (2004), and International Conference of the Learning Sciences (2004).