GET Spatial Learning Network
Through the collaboration of cognitive psychologists, education researchers, and geoscience educators, the Geoscience Education Transdisciplinary Spatial Learning Network aims to develop educational tools that can help students, across classroom and field settings, to better understand and build upon historically difficult geoscience concepts. These educational tools will be developed as a result of interdisciplinary effort, with a focus on two spatial learning principles: Spatial feedback and spatial accommodation. Spatial feedback is feedback in the form of spatial information which will allow students to not only understand that they have made an error but will also provide guidance on how to correct or reduce the error. Spatial accommodation is the adjustment needed to accommodate mental models to feedback. This accommodation can be in the form of small adjustments to a mental model, significant reconstruction of an existing model, or creation of an entirely new model. These three cases may require different types of support, and the team aims to develop tools and approaches for the range of models. The findings of this research could ultimately improve retention and learning in geosciences, with broader implications to improve retention and learning for many other Science, Technology, Engineering, and Math (STEM) domains that require spatial thinking.
Read more about our project in our NSF proposal abstract.
The Blog: Postcards from a trading zone
Throughout this project, project team members will be writing blog posts, collaboratively. Read what we're thinking on the GET Spatial Learning Blog.
Project team members are developing teaching activities incorporating spatial feedback and spatial accommodation. Browse the teaching activity collection.
Posted: Jul 3 2018
Drs. Ilyse Resnick, Kim A. Kastens, and Thomas F. Shipley published an article in the Journal of Geoscience Education, and made the cover! See their article, How students reason about visualizations from large professionally collected data sets: A study of students approaching the threshold of data proficiency.
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