Scientific Models as Guides for Disciplined Perception
2017 CSS Fellowship
Scientific data on relevant issues for our societies – global warming, vaccination, evolution theory, gene manipulation – is too complex and messy to provide clear and crisp stories that everyone agrees on. Such data is prone to cherry picking, where people can easily see what they expect to see and ignore the rest. Thus, an essential goal of science education is to teach students to be “critical observers,” i.e. participants of scientific conversations that use evidence from real-world data instead of ideologies or subjective beliefs to critically review and evaluate scientific claims. Little research exists, however, on how to best structure student experiences to teach the necessary skills and mindsets of critical observation. To study this, I will conduct two studies that address the following research questions: 1. Does the explicit comparison of scientific models with data improve participants’ ability to perceive meaningful, yet subtle patterns in rich, unstructured real-world data? 2. Compared to situations where only the target model is given, does the presence of competing models influence what evidence participants seek in the real-world data, and how they evaluate the target model?