Micro–macro compatibility: When does a complex systems approach strongly benefit science learning?

Abstract

The study explores how a complexity approach empowers science learning. A complexity approach represents systems as many interacting entities. The construct of micro–macro compatibility is introduced, the degree of similarity between behaviors at the micro‐ and macro‐levels of the system. Seventh‐grade students’ learning about gases was studied using questionnaires and interviews. An experimental group (n = 47) learned with a complexity curriculum that included agent‐based computer models, a workbook, class discussions, and laboratory experiments. A comparison group (n = 45) learned with a normative curriculum, incorporating lectures, a textbook, class discussions, and laboratory experiments. Significant learning gains and strong effect sizes were found in the experimental group's overall learning. Diffusion, density, and kinetic molecular theory were learned better with a complexity approach. Pressure, temperature, and the gas laws were learned similarly with both approaches. Learning to notice micro‐level behaviors and their probabilistic nature was greater with the complexity approach. Analysis showed that only concepts that have less “micro–macro compatibility” were learned better with a complexity approach. Thus, a complexity approach helps separate the microbehaviors and then relate them to the macrobehaviors when these behaviors are dissimilar. We discuss how micro–macro compatibility helps point to concepts whose learning would benefit strongly from a complexity approach.

Multidimensional trajectories for understanding ecosystems

“…Often, learners attend to surface, easily observable macro features and behaviors, without thinking about the less salient underlying, micro level dimensions of a system (Hmelo‐Silver et al, 2007; Hogan & Fisherkeller, 1996). One way to make unobservable features salient is through the use of simulations, and in particular, agent‐based models (Eberbach & Hmelo‐Silver, 2010; Samon & Levy, 2017). Samon and Levy (2017) note the importance of understanding the micro level and how micro and macro levels are related for understanding systems.…”

“…One way to make unobservable features salient is through the use of simulations, and in particular, agent‐based models (Eberbach & Hmelo‐Silver, 2010; Samon & Levy, 2017). Samon and Levy (2017) note the importance of understanding the micro level and how micro and macro levels are related for understanding systems. They demonstrated the importance of organizing instruction that provides a focus on the micro level with agent‐based models.…”

Glycemic control in adolescents with type 1 diabetes: Are computerized simulations effective learning tools?

“…This is especially true for adolescents who have additional physical, psychological, and social challenges. Worldwide evidence suggests that when children with T1DM move toward puberty their glucose level control deteriorates, and only 20% of T1DM patients under 15 years of age are achieving the recommended blood glucose levels . The current study demonstrates the feasibility and effectiveness of providing patient education using computerized technology.…”

Engaging students in computational modeling: The role of an external audience in shaping conceptual learning, model quality, and classroom discourse

“…Tides have low “micro–macro compatibility,” meaning that it is challenging for students to identify the microbehaviors that cause tides and relate them to aggregate patterns. Agent‐based computational modeling supports learning in such contexts because it facilitates reasoning at both micro‐ and macro‐levels (Samon & Levy, ). In this case, ABMs help students reason about the rules that water, the Earth, the Moon, and the Sun follow to form tides.…”