NCS-FO: Integrative Neural Approaches to Understanding Science Text Comprehension

The overall goal of this project from researchers at Pennsylvania State University is to understand the neurocognitive mechanisms underlying reading comprehension of expository scientific texts by school-aged children, adult first language readers, and adult second language readers. The proposed research integrates knowledge from several largely separate research traditions that are related to reading comprehension: (1) cognitive psychological and educational research in adult first language reading comprehension, (2) cognitive psychological and educational research in child first language reading comprehension, (3) neuroimaging research in text comprehension, and (4) graph-theoretical modeling of knowledge representation. Findings from this project will have significant implications for STEM education. It was funded by the Integrated Strategies for Understanding Neural and Cognitive Systems program, which included support from the EHR Core Research (ECR) program and the Behavioral and Cognitive Sciences division of SBE. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development.

The research team will study the behavioral and neural patterns during the reading of science text, in an attempt to unravel the brain's text reading network underlying first and second languages, and the neurocognitive differences between good versus poor readers. It combines methods from functional magnetic resonance imaging, cognitive study of learner abilities, and advanced data-analytic techniques in cognitive modeling and brain networks. The study of brain networks through the connectivity that exists in the functional and structural pathways of the learning brain holds the promise of providing new insights into the neural bases of individual differences, neuroplasticity, and language learning and representation. Data analytics will be applied to probe into the dynamic changes in connectivity patterns. This approach will allow the study not only of learning-induced or experience-dependent neural changes, but also what brain networks characterize individual differences in learning and representation (including intrinsic neural patterns captured by resting-state functional connectivity). Observed neural changes and patterns will allow the researchers to predict who might be more successful learners.