Adaptive Review For Mobile Mooc Learning Via Implicit Physiological Signal Sensing

Massive Open Online Courses (MOOCs) have the potential to enable high quality knowledge dissemination in large scale at low cost. However, today's MOOCs also suffer from low engagement, uni-directional information flow, and lack of personalization. In this paper, we propose AttentiveReview, an effective intervention technology for mobile MOOC learning. AttentiveReview infers a learner's perceived difficulty levels of the corresponding learning materials via implicit photoplethysmography (PPG) sensing on unmodified smartphones. AttentiveReview also recommends personalized review sessions through a user-independent model. In a 32-participant user study, we found that: 1) AttentiveReview significantly improved information recall (+14.6%) and learning gain (+17.4%) when compared with the no review condition; 2) AttentiveReview also achieved comparable performances at significantly less time when compared with the full review condition; 3) As an end-to-end mobile tutoring system, the benefits of AttentiveReview outweigh side-effects from false positives and false negatives. Overall, we show that it is feasible to improve mobile MOOC learning by recommending review materials adaptively from rich but noisy physiological signals.