Decoding individual differences in STEM learning from functional MRI data

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.

Comprehension of computer code relies primarily on domain-general executive brain regions

“…Much evidence suggests that the MD system can flexibly store task-relevant information in the short term (e.g., Fedorenko et al, 2013;Freedman et al, 2001;Shashidhara, Mitchell, et al, 2019;Wen et al, 2019;Woolgar et al, 2011). However, evidence from studies on processing mathematics (e.g., Amalric & Dehaene, 2019) and physics (e.g., Cetron et al, 2019;Fischer et al, 2016) further suggests that the MD system can store some domain-specific representations in the long term, perhaps for evolutionarily lateemerging and ontogenetically late-acquired domains of knowledge. Our data add to this body of evidence by showing that the MD system stores and uses information required for code comprehension.…”

How Does Education Hone Reasoning Ability?

“…However, newer studies have the advantage of several additional decades of refinement of cognitive theory and methodology and are well positioned to revisit this question. For example, we can ask whether completing a specific course promotes the application of newly learned rules and strategies (Halpern, 2001), changes the way we represent a problem (Cetron et al, 2019), or improves the efficiency of domain-general cognitive processes that undergird reasoning (Guerra-Carrillo & Bunge, 2018).…”

Educational fMRI: From the Lab to the Classroom

“…Accordingly, there are many examples of how fMRI research has actually informed educational theories and practices, by providing, for example, biological explanations about brain-behaviour associations during learning and development, e.g. see recent work about the brain correlates of reading acquisition (Chyl et al, 2018; Takashima et al, 2019), and conceptual knowledge in STEM learning (Cetron et al, 2019). A review of this large body of literature can be found elsewhere (e.g.…”

“…Making inferences at the system level opens new possibilities for understanding and treating brain disorders (Thiel and Zumbansen, 2016), and in understanding brain-behaviour associations. For example, it is possible to derive useful measures or scores with task-based networks to generate individual predictions about concept knowledge in STEM learning (Cetron et al, 2019).…”

“…Armed with these system-level inferences, individual abilities can be predicted, as shown recently in ADHD (Rosenberg et al, 2017), and in predicting memory performance improvement after training (Dresler et al, 2017). Recent applications of this emerging network neuroscience of learning can provide unique insights into adaptive neural processes, the attainment of knowledge, and the acquisition of new skills (Bassett and Mattar, 2017; Bogdanov et al, 2017; Cetron et al, 2019; Zhang et al, 2019). Many studies have highlighted the usefulness of network analyses and the possibility to derive solid biomarkers of brain disorders (Bassett and Bullmore, 2009; Braun et al, 2018; Du et al, 2018); for example for reading difficulties (Bailey et al, 2018; Edwards et al, 2018) and autism (Hong et al, 2019).…”