There are many conceptual difficulties in understanding climate change. Some arise from the embedded science concepts (Shepherdson, Choi, Niyogi, & Charusombat, 2009). Others relate to the inherent causal complexity, such as domino, cascading, and cyclic causal patterns with feedback loops and tipping points. This complexity includes thinking about patterns of space and time very differently: analyzing at different scales; including spatial gaps and time delays; and monitoring steady states rather than focusing on event-based causality.
Students have difficulty reasoning about causal complexity (e.g. Chi, 2005; Grotzer, 2003; Hmelo-Silver & Pfeffer, 2004; Wilensky & Resnick, 1999) in the context of climate change (Grotzer & Lincoln, 2007; Sterman & Booth Sweeney, 2002). Reasoning about deep time is especially problematic (Dodick & Orion, 2003a; 2003b)—students have little notion of how to contrast concepts that happen over billions of years to those that happen in more familiar time spans. In the context of climate change, time scales impact how we interpret patterns and can influence whether people believe the scientific evidence relevant to climate change. In this work, we asked, “What kinds of learning supports can help students reason about climate change over time?”
Working with an eighth grade class in an urban, low SES, middle school, we investigated learning supports to help students reason about their questions, “Why do some people think that climate change isn’t really happening?” “If it is so cold and snowy, how can climate change be happening?” Using a design research methodology, in which students’ understanding was assessed and then learning supports were designed and tested with iterative assessment, we documented class conversations and students’ individual responses to questions about time and scale.
Results/Conclusions
We introduced an analogous problem in which students contrasted three “snapshots” of the batting average of a
