Developing a Brain-Computer Interface to Enhance Learning

Danielian, David

Student Work

Developing a Brain-Computer Interface to Enhance Learning

Public Deposited

Brain-Computer Interfaces (BCIs) show promise in helping enhance the way we use software. By using machine learning models, it is possible to identify states such as proactive control, reactive control, rule acquisition, rule-following, mind-wandering. In the field of education, having insight into the cognitive state of the user means we can better understand why students are struggling. In this project, we seek to bridge the gap between online learning platforms and brain data, to enable the creation of BCI that utilize brain data to improve students learning outcomes. After conducting a persona study and discussing with researchers, we created BrainBridge a browser extension and API which facilitates communication between online learning platforms and brain data neuroimaging headsets. BrainBridge is capable of visualizing and providing realtime interventions based on a users cognitive state, while also logging actions in the learning platform and mapping them to brain data. BrainBridge was built and tested with the ASSISTments learning platform and fNIRS neuroimaging cap in mind, but has the potential to be expanded to support other neuroimaging techniques and online learning platforms.

This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

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