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Preliminary Steps Towards Detection of Proactive and Reactive Control States During Learning with fNIRS Brain Signals

This paper describes a two-pronged approach to creating a multimodal intelligent tutoring system (ITS) that leverages neural data to inform the system about the student’s cognitive state. The ultimate goal is to use fNIRS brain imaging to distinguish between proactive and reactive control states during the use of a real-world learning environment. These states have direct relevance to learning and have been difficult to identify through typical data streams in ITSs. As a first step towards identifying these states in the brain and understanding their effects on learning, we describe two preliminary studies: (1) we distinguished proactive and reactive control using fNIRS brain imaging in a controlled continuous performance task and (2) we prompted students to engage in either proactive or reactive control while using an ITS to understand how the two modes affect learning progress. We propose integrating the fNIRS datastream with the ITS to create a multimodal system for detecting the user’s cognitive state and adapting the environment to promote better learning strategies.