Abstract
Learners often struggle to grasp the important, central principles of complex systems, which describe how interactions between individual agents can produce complex, aggregate-level patterns. Learners have even more difficulty transferring their understanding of these principles across superficially dissimilar instantiations of the principles. Here we provide evidence that teaching high school students an agent-based modeling language can enable students to apply complex system principles across superficially different domains. We measured student performance on a complex systems assessment before and after one week training in how to program models using NetLogo (Wilensky, 1999a). Instruction in NetLogo helped two classes of high school students apply complex systems principles to a broad array of phenomena not previously encountered. We argue that teaching an agent-based computational modeling language effectively combines the benefits of explicitly defining the abstract principles underlying agent-level interactions with the advantages of concretely grounding knowledge through interactions with agent-based models.
Highlights
Specialty section: This article was submitted to Educational Psychology, a section of the journal Frontiers in Education
We argue that teaching an agent-based computational modeling language effectively combines the benefits of explicitly defining the abstract principles underlying agent-level interactions with the advantages of concretely grounding knowledge through interactions with agent-based models
We propose that instruction in an agent-based computer programming language can create complex systems knowledge that is transportable among superficially distant domains
Summary
Specialty section: This article was submitted to Educational Psychology, a section of the journal Frontiers in Education. We provide evidence that teaching high school students an agent-based modeling language can enable students to apply complex system principles across superficially different domains. Whereby learners apply knowledge learned in one domain to a superficially different, novel domain, may be especially difficult for complex systems understanding because different instantiations of complex systems often share few, if any, superficial features. Modeling Complex Systems systems approaches, which enable researchers to study phenomena that have multiple causes and consequences and have structure at many different temporal, spatial, and organizational levels, have had a large impact on the fields of math and science (e.g., Deneubourg et al, 1986; Forrest, 1991; Dawkins, 1996; Epstein and Axtell, 1996) and are having an increasing impact on engineering, medicine, finance, law, and management (Jacobson and Wilensky, 2006). Despite the widespread influence of complex systems approaches in science and engineering, the tools and perspectives of complex systems have had significantly less influence in STEM curriculum (Jacobson and Wilensky, 2006)
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