Breeding Birds to Learn about Artificial Selection: Two Birds with One Stone?

Abstract

Recent research in Learning Sciences has drawn attention to the affordances of enabling students to learn about scientific phenomena through a complex systems lens. In this study, we adopt a complex systems perspective in helping students to learn about artificial selection by using an agent-based participatory simulation - Bird Breeder. Our goal is to identify the strategies students used when using this simulation, and investigate the kinds of abstractions they made about the underlying agent-level mechanisms and emerging population-level outcomes of the model. We answer these questions using data collected from three high school biology classes that used this simulation. Our findings indicate that Bird Breeder was effective in fostering learning about the agent-level mechanisms that drive artificial selection. In contrast, the effectiveness of Bird Breeder at helping students learn about population-level outcomes of artificial selection is less clear. The findings have provided insight into possible design revisions to this simulation.