Whole Group Discussion
Things We Haven't Heard Enough About
True, solid geoscience data is hard to access within our visualizations.
Every visualization we've heard about so far has been about "the known"—and not enough about the unknown. Its possible to make visualizations of the unknown. Geoscientists do it, and we should pursue this in our educational materials. The unknown is where science happens. Visualizations can be used, for example, to generate hypotheses.
Would have liked more discussion about the visualization of minerals, etc. at a really small scale. Then scaling up from minerals to rocks and formations would be of interest.
Arguing from incomplete data is an important skill and practice of field geologists. We may want to muddy up the clinically perfect visualizations. Want to argue from known but incomplete data.
Photographs. Want to think about the role of real photos.
Talked a lot about space and time, but didn't talk a lot about the connection between small scale and large scale data. Making arguments from very small minerals to influence our interpretation of larger scale processes (e.g., oil expulsion).
What do we do with our visualizations to accommodate students with special needs (dyslexia, visual impairment)?
Causality—How can we represent concepts and other "non-visibles" like causal connections (e.g., between the air temperature and rates of evaporation)?
Visualization of uncertainty and data quality.
Using earth science data as a resource for student inquiry activities (mineral formation data, seismic data, salinity of ocean locations). Perhaps could think more deeply about how to present the data to students (e.g., specialized tools).
How to connect the ways in which visualizations are used across disciplines (physics, chemistry, biology, math)? Want to avoid clashes and leverage similarities.
Problem of reconciling pedagogical goals with pragmatics of university instruction ("how many points is this worth").
Would like to have more input from the existing literature about how students learn from models and visualization.