Abstract
Despite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals. Specifically, we hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner’s neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student experts in computer science. We show that alignment among students successfully predicts overall performance in a final exam. Furthermore, within individual students, we find better learning outcomes for concepts that evoke better alignment with experts and with other students, revealing neural patterns associated with specific learned concepts in individuals.
Highlights
Despite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings
Did alignment to canonical neural representations emerge during learning, and did alignment reflect successful learning? To address these questions, we examined neural activity patterns and learning outcomes in undergraduate students and in graduate experts
We found that knowledge structure alignment was positively correlated with exam scores across the hippocampus, anterior cingulate cortex (ACC), angular gyrus and temporal regions of interest (ROIs) when derived for the student cohort
Summary
Despite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. We hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner’s neural representations match those of experts, as well as those of other learners We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. Neural classifiers were trained to predict an expertdefined category label (cantilevers/trusses/vertical loads) for each item based on imaging data These enabled the authors to detect individual differences in understanding the physics concept of Newtonian forces. Specific concepts have been shown to evoke similar neural activity patterns across individuals, suggesting a shared structure for neural representations[2,24,25,26,27] This body of work suggests that shared neural responses reflect thinking alike. In the context of learning, the students’ goal could be viewed as laying the neural foundation that would allow them to think like experts
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