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Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions
Author(s) -
HmeloSilver Cindy E.,
Pfeffer Merav Green
Publication year - 2004
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog2801_7
Subject(s) - perspective (graphical) , construct (python library) , psychology , complex system , coding (social sciences) , cognitive science , function (biology) , mental representation , computer science , cognitive psychology , cognition , artificial intelligence , mathematics , statistics , evolutionary biology , neuroscience , biology , programming language
Complex systems are pervasive in the world around us. Making sense of a complex system should require that a person construct a network of concepts and principles about some domain that represents key (often dynamic) phenomena and their interrelationships. This raises the question of how expert understanding of complex systems differs from novice understanding. In this study we examined individuals' representations of an aquatic system from the perspective of structural (elements of a system), behavioral (mechanisms), and functional aspects of a system. Structure–Behavior–Function (SBF) theory was used as a framework for analysis. The study included participants from middle school children to preservice teachers to aquarium experts. Individual interviews were conducted to elicit participants' mental models of aquaria. Their verbal responses and pictorial representations were analyzed using an SBF‐based coding scheme. The results indicated that representations ranged from focusing on structures with minimal understanding of behaviors and functions to representations that included behaviors and functions. Novices' representations focused on perceptually available, static components of the system, whereas experts integrated structural, functional, and behavioral elements. This study suggests that the SBF framework can be one useful formalism for understanding complex systems.