Discussion Prompts:
1) Which is more important to you as teachers, fully-described, multi-day/week modules that pull together multiple lines of evidence as a project or the components to put together one's own modules (animations, demonstrations, 1-2 day lessons)? eg, what types of resources do you need? Please elaborate on your choice thinking about the pros and cons of each.
2) What strategies do you employ for helping students work with complex data and models? How can do you help students to critically think about data and models, looking at evidence for themselves rather than simply trusting the conclusions of others?

Regarding Question 2: I've found that there is a great deal of confusion regarding the concepts of "evidence", "data", "conclusions", and "models". Like many ideas in science there are colloquial meanings for the terms that are not in all ways consistent with more specific, technical meanings (a good example is the term "bug", which in everyday use means any crawly thing, while it has a specific meaning to entomologists--a hemipteran). I've found it important to explore the meanings of those ideas with students (and teachers) as we begin to look at those relationships. "Models" is especially challenging as in common use it often refers to a "type" (e.g., car model) or an ideal (e.g., fashion model, model citizen). Even in science, the everyday use of model is limited to a physical representation that differs only in terms of scale (i.e. larger or smaller than the original). I find that the idea of mathematical models--representing the states and/or processes of a system quantitatively--is not familiar and takes a fair bit of work to get used to. The same is true with what is meant by the other terms I mentioned above. The challenge is to find ways to explore their meanings so students understand their attributes as they begin trying to generate them for themselves. I'd welcome ideas about how to do that.

Hi Ed, I couldn't agree with you more. Understanding what computational models represent is new for teachers and students. It does take work to think about the limitations of these models, and to get used to the idea that in order to evaluate a model you need to compare it to other evidence. So, you see a trend in the computer model, does it reflect the data the scientists have collected? Where does it diverge, why that might be? Where is it similar, what is it therefore representing? Even beginning to explore this gets students thinking more about how conclusions can be drawn from models.