Modeling Brain and Behavior to Uncover the Eye-Brain-Mind Link during Complex Learning

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The project, based at the University of Colorado, will advance fundamental knowledge on learning from complex STEM texts. This is a critical skill for success in an increasingly information-driven world and workforce, but it is also an area where students consistently struggle. Scores on standardized assessments are stubbornly stagnant, troublesome achievement gaps remain, and the U.S. continues to lag behind its international peers. This is especially relevant to the reading of complex STEM content. The main idea of the project is that learning from text is fundamentally about the coordination of vision (the eye) with thought (the mind) in a manner constrained by the content (the text), the context (the task), and the individual (the learner). Thus, there is a need to understand how these factors interact in order to advance basic knowledge and to inform effective interventions. The team will study how brain and behavioral signals can be used to understand the reading process and associated learning outcomes in a manner that is sensitive to individual differences. Anticipated near-term broader impacts include the interdisciplinary training of students, especially those from groups underrepresented in STEM fields, community outreach, and promoting scientific reasoning skills relevant to multiple STEM areas. In the longer term, the results of the project will inform the design of future interventions that aim to make learning from text more efficient, engaging, and effective. The project is funded by the EHR Core Research (ECR) program, which supports work that advances the fundamental research literature on STEM learning.The intellectual merit of the project is to gain fundamental knowledge on how the eye and brain dynamically coordinate to construct mental representations during complex learning from STEM texts. The project will address this goal by collecting eye movements, reading behaviors, noninvasive brain signals, and learning outcomes from high-school and college students in controlled lab settings and in school environments. It is expected that students will improve critical thinking and scientific reasoning skills by engaging with multiple texts on scientific research methods. The project will use behavioral (eye tracking and reading times) and non-invasive brain measures (high-density functional near-infrared spectroscopy and electroencephalography) to identify neurobehavioral markers of core processes involved in reading comprehension (e.g., focused attention vs. mind wandering, inference generation and elaboration) and the extent to which these measures can predict learning outcomes (comprehension, knowledge, and transfer) assessed immediately after learning and a week later. The project will also leverage advances in deep machine learning to build computational neurobehavioral models of the process and outcome variables in a manner that is sensitive to textual difficulty and to individual differences (e.g., prior knowledge, interest). Thus, the project will advance fundamental knowledge by integrating three research areas: theories of low-level reading processes and associated models of eye movements, models of higher-order STEM text comprehension including multiple document comprehension, and emerging research on the cognitive neuroscience of reading including co-registration with eye movements.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.