Agent-based modeling can get students thinking about mechanism. But to get students refining their models toward accurate explanations, comparing models with data is just as important.
Fuhrmann, T., Rosenbaum, L., Wagh, A., Eloy, A., Wolf, J., Blikstein, P., & Wilkerson, M. H. (2024). Right but wrong: How students’ mechanistic reasoning and conceptual understandings shift when designing agent-based models using data. Online First in Science Education. doi: 10.1002/sce.21890
Abstract. When learning about scientific phenomena, students are expected to mechanistically explain how underlying interactions produce the observable phenomenon and conceptually connect the observed phenomenon to canonical scientific knowledge. This paper investigates how the integration of the complementary processes of designing and refining computational models using real-world data can support students in developing mechanistic and canonically accurate explanations of diffusion. Specifically, we examine two types of shifts in how students explain diffusion as they create and refine computational models using real-world data: a shift towards mechanistic reasoning and a shift from noncanonical to canonical explanations. We present descriptive statistics for the whole class as well as three student work examples to illustrate these two shifts as 6th grade students engage in an 8-day unit on the diffusion of ink in hot and cold water. Our findings show that (1) students develop mechanistic explanations as they build agent-based models, (2) students’ mechanistic reasoning can co-exist with noncanonical explanations, and (3) students shift their thinking toward canonical explanations after comparing their models against data. These findings could inform the design of modeling tools that support learners in both expressing a diverse range of mechanistic explanations of scientific phenomena and aligning those explanations with canonical science.
