Decoding individual differences in STEM learning from functional MRI data

Joshua S. Cetron, Andrew C. Connolly, Solomon G. Diamond, Vicki V. May, James V. Haxby and David J. M. Kraemer ()
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Joshua S. Cetron: Department of Education, Dartmouth College
Andrew C. Connolly: Dartmouth College
Solomon G. Diamond: Dartmouth College
Vicki V. May: Dartmouth College
James V. Haxby: Dartmouth College
David J. M. Kraemer: Department of Education, Dartmouth College

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: Abstract Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural ‘score’ to complement traditional scores of an individual’s conceptual understanding. Using a novel data-driven multivariate neuroimaging approach—informational network analysis—we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science.

Date: 2019
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DOI: 10.1038/s41467-019-10053-y

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