Autor: Sabrina Gado

Monitoring learners’ mental states via a passive Brain-Computer Interface (BCI) allows to continuously estimate current abilities, available cognitive resources, and motivation. It bears the great potential to adapt educational contents, learning speed, and format to the learner’s needs via an intelligent tutoring system. We present a neurophysiological-based approach to continuously monitor learners’ current affective-emotional and cognitive states by measuring and decoding brain activity via a passive BCI. In two studies (N = 8 and N = 7), we investigate whether we can a) predict learners’ affective and cognitive states during a learning or training session, b) provide continuous feedback of recognized states to the learner and, thereby, c) increase performance and intrinsic motivation. Oscillatory power measures in the alpha (8 – 12 Hz) and theta (4 – 7 Hz) frequency band served as features for the prediction and visualization. Our results reveal that machine learning ...