The brain organization of STEM concept knowledge: a neurally-based foundation for training, measuring, and assessing concept learning from basic knowledge to expertise

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

The premise of this project is that an understanding of how STEM concepts are organized in the brain would enable the enhancement of STEM concept learning and its assessment. Research has shown that although students vary in how accurately or completely they acquire knowledge related to a STEM concept, their neural filing systems are remarkably similar, in terms of which brain systems are the sites of particular aspects of concept knowledge. Understanding this neural organization common to everyone makes it possible to take that organization into account in the course of instruction. In effect, it makes it possible to “teach to the brain”. More specifically, it makes it possible to develop innovative cognitive training techniques based on modern machine-learning guided neuroscience to supplement traditional STEM learning. The goal is to formulate a detailed theory of how basic STEM concept knowledge in multiple STEM disciplines is neurally organized, how the underlying organization develops with learning, and how the organization is impacted by instructional and ability factors across STEM domains. Findings from this project would advance the understanding of the ontology of scientific concepts, theories of instructional design, and AI (machine-learning) guided instruction. Altogether these advances would facilitate more effective interventions towards expert-level knowledge. The design of this project will purposefully include both University and Community College students with a large range of STEM abilities in traditionally under-studied groups. This project will assess the brain representations of STEM concepts (using several fMRI measures) in 4 different domains (physics, biology, chemistry, and mathematics) in students at multiple levels of expertise and assess the changes in those representations using machine-learning analysis of fMRI data under different types of instruction (class instruction, in-lab concept instruction, and expertise-focused training). The goal of the instruction will be to generate neural representations in novice learners that are similar to those of instructors or domain experts. This project builds on the investigators’ prior NSF funded work which demonstrated that fMRI can identify the underlying neural dimensions of physics concepts, and can predict and assess learning of these concepts (more so than traditional behavioral measurements). This research continues investigation of how neural data can usefully guide instruction by extending the approach to additional STEM domains and by guiding cognitive instruction with the accompanying neural assessment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.