Making sense of models: How teachers use agent‐based modeling to advance mechanistic reasoning

Abstract

AbstractComputer modeling promotes mechanistic reasoning when learners build and analyze models of complex systems to explore causal mechanisms and use models to generate patterns. StarLogo Nova (SLN), an agent‐based modeling (ABM) environment, enables novice programmers to model a system's individual components and investigate its emergent, collective behavior. Through case analysis of teachers using SLN, we demonstrate how ABM advances thinking about mechanisms generating phenomenon. Teachers who used simulation combined with the decoding of SLN models utilized mechanistic reasoning to make sense of how and why complex phenomenon emerged.Practitioner NotesWhat is already known about this topic Mechanisms, or causal explanatory processes, are critical in science. Mechanistic reasoning is concerned with why a phenomenon occurs. Traditionally in science classrooms, teachers incorporate pre‐packaged simulations that limit the ability to understand mechanisms within the simulations. Agent‐based modeling empowers learners to model at the micro‐level to understand how macro‐level phenomenon emerge in complex systems. What this paper adds StarLogo Nova, an agent‐based modeling environment, has features that support mechanistic reasoning. Using simulation combined with decoding of models in StarLogo Nova promotes mechanistic explanations of why phenomenon occurs. Teachers are able to scaffold student interactions in StarLogo Nova to support students' mechanistic reasoning. Implications for practice and/or policy When using computer simulations in instruction, incorporate decoding of models to engage students in uncovering why a phenomenon occurs. In professional development programs on scientific modeling, it is important for educators to learn how decoding contributes to students' understanding of mechanisms in models and experience modeling as a scientific practice.