Maths Anxiety and cognitive state monitoring for neuroadaptive learning systems using electroencephalography

Mathematical competence is important to acquire for everyday and professional purposes, but often represents a considerable hurdle for students, who may associate it with unpleasant experiences. Our goal is to use neuroscience and neural engineering to support students to improve their mathematical understanding. More specifically, we are interested in the development of a non-invasive electroencephalogram (EEG)-based neuroadaptive Brain-Computer Interface (BCI) learning environment that optimizes learning outcomes by adapting the learning content provided according to the cognitive load of the learner. In this paper, we investigate what cognitive states occur when students with and without Math Anxiety learn to solve a math problem presented in the form of a novel computer puzzle. Results of an offline analysis of data recorded from 10 study participants suggest that different cognitive states occur, each with specific features that a BCI could potentially detect.