Detection and Asynchronous Flow Prediction in a MOOC

Flow is a human psychological state positively correlated to self-efficacy, motivation, engagement, and academic achievement. In a MOOC, flow detection and prediction would potentially allow for learners’ content personalization, fostering engagement and increasing already-low completion rates. In this study, we propose a Machine Learning flow-predicting model by pairing the results of the EduFlow-2 and Flow-Q measure instruments issued to participants of a MOOC (n = 1589, 2-year data collection). The resulting flow-predicting-model detects flow in an automatic, asynchronous fashion by applying only the EduFlow-2 measurement instrument. Our model proposal predicts flow presence with greater precision than it detects flow absence.