Spatial thinking is a powerful driver of success in the STEM classroom and spatial thinking is a major predictor of future STEM success in the workforce. Through a partnership with local Virginia public school systems, we can study the effects of a novel curriculum (the Geospatial Semester) aimed at teaching high school students principles of spatial reasoning, data analysis, and computer science through GIS mapping software. Our team works collaboratively with experts in the field and the high school teachers to refine and enhance this curriculum and understand its effect. We collect functional magnetic resonance imaging (fMRI) and behavioral data from students before and after participating in this course. In particular, we are interested in investigating the transfer of the spatial reasoning skills taught directly in the course to other forms of reasoning including verbal. We are also collecting fMRI and behavioral data from a group of highly experienced and specially trained teachers as an expert standard to determine neural markers of students’ conceptual knowledge and spatial relational reasoning. This project is also testing whether it is possible to compare different curricula based on how much they strengthen the representation of concepts in the brain. 

Data from previous cohorts have indicated that students enrolled in the Geospatial Semester curriculum demonstrated improved verbal reasoning. Analysis showed that students’ spatial cognition gains predicted and mediated their reasoning improvement indicating a spatial basis for verbal transfer. Longitudinal fMRI demonstrated learning-related changes in neural activity, connectivity, and representational similarity in spatial cognition-implicated reasoning. These neural changes predicted and mediated transfers from spatial cognition gains to verbal reasoning.  

Cortes, R., Peterson, E., Kraemer, D., Kolvoord, R., Uttal, D., Dinh, N., Weinberger, A., Daker, R., Lyons, I., Goldman, D., & Green, A. (2022). Transfer from Spatial Education to Verbal Reasoning and Prediction of Transfer from Learning-Related Neural Change. Science Advances, 8(31), eabo3555. PDF