Wearable Sensing and Quantified-self to explain Learning Experience

Abstract

The confluence of wearable technologies for sensing learners and the quantified-self provides a unique opportunity to understand learners' experience in diverse learning contexts. We use data from learners using Empatica Wristbands and selfreported questionnaire. We compute stress, arousal, engagement and emotional regulation from physiological data; and perceived performance from the self-reported data. We use Fuzzy Set Qualitative Comparative Analysis (fsQCA) to find relations between the physiological measurements and the perceived learning performance. The results show how the presence or absence of arousal, engagement, emotional regulation, and stress, as well as their combinations, can be sufficient to explain high perceived learning performance