Lessons from the Modeling Lab

The three Nersessian articles for this week provide several examples of modeling as a scientific practice. Her works provide case studies of laboratory engineers – often with no background in the particular branch of science that they are modeling – who, through modeling, ultimately produce novel scientific concepts.

Nersessian et al.’s work sheds light on the various ways these researchers use modeling in their practices and the ways modeling distributes cognition across the laboratory’s system. In “Building Simulations from the Ground Up,” MacLeod and Nersessian discuss how researchers start to build models of systems that do not have articulated theoretical frameworks. These researchers instead pieced together relevant bits of information about the system and assembled them in a “nest-like fashion” to create a coherent initial model that could be refined later (540). In the “Coupling Simulation and Experiment” article, MacLeod and Nersessian follow researchers who take a bimodal approach and model and experiment simultaneously (unlike those modelers whose experimental data comes from outside collaborators). They then describe how the researchers are able to iteratively tune their model toward their experimental results, in a process not unlike Pickering’s dance of agency. Finally, in the “Building Cognition” paper, Chandrasekharan and Nersessian explore how the model representation becomes coupled to the modeler’s imagination through the construction process, and in turn how this coupling cultivates the modeler’s cognitive powers and enables discovery.

I found the “Building Cognition” piece to be the most interesting, because it was motivated by a desire to understand how novices could make elusive scientific discoveries while working with digital media, such as FoldIt. Chandrasekharan and Nersessian attempt to answer this question by drawing a parallel to modelers without knowledge of the biological pathway they have been tasked to model, and following their discovery process. I don’t know if that parallel was fully justified; after all, these researchers are expert modelers, even though they’re not expert biologists. So, when they describe the cognitive powers that modeling activities helped develop, I couldn’t help but wonder how much experience was actually needed to develop these powers. I suppose it’s the same question we had while reading the Wilensky pieces, though mildly adapted: what information are we missing that would be necessary to make students’ performance with modeling this successful?

“Building Simulations from the Ground Up” resonates with Wilensky’s “Thinking Like a Wolf, a Sheep, or a Firefly.” Students, like the engineers, started modeling their systems without an underlying theory (beyond their own embodied observations) and later refined their models by synthesizing information from relevant literature. I did not feel like the bimodal article had a lot of overlap with the other pieces we’ve read so far, in that we haven’t read an account of students who created a model of a system and experimented on that system in tandem. However, it is reminiscent of Ashlyn’s anecdote of asking her students to observe ant behavior before they modeled the ant activity computationally; I also think of my time in an undergraduate physics lab where we needed to draw a model of a circuit that was hidden within a blackbox by performing experiments on the circuit and taking measurements.

However, I don’t remember doing much of that in my K-12 education. In high school, my labs were mostly procedural, with instructions about what information to graph and a handful of questions at the end that required declarative answers. I wonder what (almost) simultaneous modeling and experimentation would look like in the classroom. I imagine that it would need a lot of scaffolding activities to foster the development of the cognitive powers outlined in the “Building Cognition” piece.