This project explores the use of measurements of brain activity from lightweight brain sensors alongside student log data to understand important mental activities during learning. Bringing together cognitive neuroscience, computer science, and STEM education, the project will build a better understanding of when and how learning occurs during tutoring system use, enabling the creation of adaptive interventions within tutoring systems that are better personalized to the needs of the individual.
Faculty Advisor: Erin Solovey | WPI (Computer Science)
Teacher Component: Teachers will be trained in human-subjects experiment design and execution, including methods for non-invasive, lightweight, portable brain imaging with functional near-infrared spectroscopy. They will be involved with running studies in human subjects. Once data has been collected, teachers will be exposed to machine learning approaches for automatically classifying relevant learner states from brain data.