Think Like an Expert: Neural Alignment Predicts Understanding in Students Taking an Introduction to Computer Science Course

Abstract

How do students understand and remember new information? 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 measure for predicting and assessing learning outcomes. 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 understanding is mirrored in "neural alignment": the degree to which an individual learners 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 found that alignment among students successfully predicted overall performance in a final exam. Furthermore, within individual students, concepts that evoked better alignment with the experts and with their fellow students were better understood, revealing neural patterns associated with understanding specific concepts. These results provide support for a novel neural measure of concept understanding that can be used to assess and predict learning outcomes in real-life contexts.