Classroom Learning of Concepts and Reasoning Strategies
Fostering true conceptual understanding and effective reasoning strategies in the minds of students, not just the right answers on the test, is a goal of teachers in every science, technology, engineering, and mathematics (STEM) classroom. Recent advances in tools used to analyze images of the human brain allow detection of complexly-patterned changes in the brains of students that signify learning of STEM concepts and STEM-relevant relational reasoning strategies. This advance may open a window onto biomarkers of precisely the type of learning that is the goal of educators. Critically, this new approach has not yet been applied to longitudinal learning in a real-world classroom, i.e., how the brain changes over time during schooling and how those brain changes relate to changes in knowledge and thinking skills. If it is possible to observe brain changes that correspond to classroom-based strengthening of concepts and thinking strategies, then – in conjunction with traditional assessment methods – this method has transformative potential to help identify the most effective real-world educational practices, and could profoundly influence the future use of brain imaging in education and education research.
A good test case for this approach is education that supports spatial thinking. 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. The brain systems that support spatial thinking have been well mapped by neuroscience to allow clear interpretation of new brain-imaging data. We are conducting cross-disciplinary projects that bring together experts in geoscience classroom education, spatial cognition, and neural bases of learning and reasoning. This team is committed to bridging the conspicuous gap between the cognitive neuroscience laboratory (where many insights into learning have been gained) and the real-world high school classroom (where the neural mechanisms of learning have gone largely unexplored). A confluence of advances in neuroimaging, and our research team’s partnership with Virginia school systems make this effort timely and tractable. This research is also providing new data that have potential to impact broadening participation in STEM.
These projects are directed toward a new level of understanding of the neural mechanisms of spatial learning (how spatial learning changes the brain), to promote adoption of spatial education, and to identify factors that impact disparities in STEM learning and participation (e.g., sex, STEM-related anxieties). We are collecting functional magnetic resonance imaging (fMRI) and behavioral data from students before and after learning in a high school geoscience course that uses a novel spatially-based curriculum to teach STEM concepts and spatial reasoning. Data we have already obtained on this spatial curriculum have begun to characterize the underlying cognitive and neural mechanisms at work, and show promising effects of transfer to STEM problem solving and core measures of spatial ability. Consistent with methods that have demonstrated success in the lab (but not yet the classroom), we are using neural representations of 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. Leveraging, recent multivariate pattern analysis (MVPA) and machine-learning advances in brain imaging, we can compare the neural patterns of students before and after learning to test for a trajectory that moves students closer to expert representations. These projects are also testing whether it is possible to compare different curricula based on how much they strengthen the representation of concepts in the brain. Likewise, this work is testing whether spatial education leads students to engage spatial brain resources more effectively for STEM-related reasoning, and comparing curricula on this basis. One of our goals in these projects is to test whether neural data add predictive value to traditional testing (e.g. conventional unit tests) for subsequent retention of conceptual knowledge and spatial reasoning. Additionally, assessments of STEM-related anxieties (e.g., math and spatial anxiety) and analyses of sex-related effects on cognitive and neural outcomes in this project have the potential to characterize factors that influence disparities in STEM learning and participation.