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

Abstract

AbstractThe 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.