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(This post is adapted from a talk I gave on "Teaching Complex Earth Systems Using Visualization" at the Cutting Edge workshop on "Developing Student Understanding of Complex Systems in the Geociences." My powerpoint and those of the other speakers can be downloaded from the online program.)In a previous post, Universal versus Conditional Truths, I made the case that concept-driven visualizations in earth sciences can lead students and other viewers to underappreciate how much variation there is in the earth system. The scientists and illustrators who create such diagrams must make many decisions about what to include and how to depict those feature that they do include. Of necessity they typically leave out more options than they include.
Today I would like to explore the possibility that the entire community of people who create concept-driven visualizations are collectively under-representing the range of possibilities in the earth system. More
Two posts back, I introduced the distinction between data-driven and concept-driven visualizations, and in the last post I explored some of the affordances and pitfalls of concept-driven visualizations. Today I'd like to dig into how data-driven visualizations get made in geosciences--and how much of that process students need to know about. Recall that "a data-driven visualization uses empirically or mathematically derived data values to formulate the visualization" (Clark & Wiebe, 2000, p. 28.)
With doctoral student Sandra Swenson, I have been researching how middle school and high school students understand one particular data-driven visualization: a global map of topography and bathymetry.
(Adapted from reflective essay written for the DFG/NSF Spatial Cognition Workshop July 2009, New York)
Extracting meaning from spatial data does not come easily for many students. On its surface, a geospatial representation comprises dots, squiggles and blotches of color. The process of turning these dots, squiggles and blotches into a scientific explanation seems woefully underconstrained. Where is a student to start? How is it that skilled spatial thinkers can construct meaningful inferences about causal processes from observations of shape, size, position, orientation, configuration or trajectory of objects or phenomena? What scaffolding can an educator put in place to help a mystified student begin to think methodically and productively about spatial data, without simply telling them the answer?
I suggest that it may be possible and useful to equip such students with a suite of "hypothesis templates" that correspond with distinctive, frequently-observed spatial patterns. More
In the previous post, I mentioned mental rotation, that test where you have to say if one shape is the same as another shape except for having been rotated. Mental rotation is one of the most widely used tests of spatial abilities, with a long history and extensive literature.
A geoscience task that seems to me somewhat like mental rotation is learning to identify microfossils in a microscope slide. The examples in the reference books are all lined up neatly, with individuals of closely related species all oriented the same way to make it easier to spot the definitive differences. The individuals on the microscope slide are turned every which way.
Do micropaleontologists use mental rotation? Do novices go through a phase where they use mental rotation to compare unknown individuals with illustrated type specimens? Do experts eventually develop perceptual short cuts that allow them to bypass the cognitively demanding mental rotation step? (I wouldn't know. I was always pathetic at fossil ID.) How could these questions be researched?
At the spatial cognition workshop I mentioned in the previous post, we were asked to think big picture thoughts about what we most wanted to find out about spatial cognition. My big wish is to be able to find out the energy costs, literally the calorie expenditure, of various thought processes.
This notion is not totally far fetched. Functional magnetic resonance imaging (fMRI), the brain imaging technology that shows specific regions of the brain "lit up" when they are being used, is a measure of blood flow. Blood flows in order to bring oxygen. Oxygen comes in order to support respiration. And respiration occurs in order to generate energy. So at least conceptually, the increase in blood flow that occurs when a brain region is active could be a proxy for energy demand. More