The Potential Use of Neurophysiological Signals for Learning Analytics

Abstract

Learning analytics is a very promising field for improving education, teaching and learning by collecting user data in serious games. However, the research on the use of physiological traces in learning analytics is sparse. Therefore, in this empirical explorative study we used functional near-infrared spectroscopy to examine if neurophysiological measurements can help to identify if a user is learning during playing a simple game. We identified increased brain activation in fronto-parietal areas while users actively learned rules or applied knowledge during gaming. In contrast, in a random condition, where users only reacted to the game without the possibility to learn or apply knowledge, increased brain activation was observed in central motor areas of the brain, reflecting the motoric interaction with the game per se. This preliminary neurophysiological empirical study showed that it is possible to use neurophysiological data to get insight on user learning during playing a very simple game. Future studies have to examine this effect in a more complex learning situation.