EEG markers of STEM learning

In this Innovative Practice Full Paper, we examined whether signals from inexpensive, wearable brainwave sensors could be used to identify the STEM learning task in which a student was engaged. Twelve subjects completed four different STEM learning tasks - two entailing passive learning (watching a video or reading), and two entailing active learning (solving problems based on the passive learning). There were two mathematics tasks (one active and one passive) and two Python programming tasks (one active, one passive). Subjects were fitted with wearable brainwave sensors that captured cortical oscillations from four scalp electrodes, and transformed the signals from each electrode into five distinct frequency bands. This yielded 10 samples per second within each frequency band and from each electrode. We then trained ensemble-based machine learning algorithms (boosting and bagging of decision tree learners) to classify various features of tasks and subjects from a single sample of brainwave activity. We explored several different types of training/testing regimes, and our results suggest that within a single session, brain activity patterns for each of these four types of learning are substantially different, but that the patterns do not generalize well between sessions. Importantly, the brainwave patterns differ greatly between individuals, which suggests that applications using such devices will need to rely on personalization to achieve high accuracy. The project is a first step toward developing apps that could use individualized EEG feedback to help subjects develop learning strategies that optimize their learning experience.