AttentiveLearner: Improving Mobile MOOC Learning via Implicit Heart Rate Tracking [chapter]

Phuong Pham, Jingtao Wang
2015 Lecture Notes in Computer Science  
We present AttentiveLearner, an intelligent mobile learning system optimized for consuming lecture videos in both Massive Open Online Courses (MOOCs) and flipped classrooms. AttentiveLearner uses on-lens finger gestures as an intuitive control channel for video playback. More importantly, At-tentiveLearner implicitly extracts learners' heart rates and infers their attention by analyzing learners' fingertip transparency changes during learning on today's unmodified smart phones. In a
more » ... nt study, we found heart rates extracted from noisy image frames via mobile cameras can be used to predict both learners' "mind wandering" events in MOOC sessions and their performance in follow-up quizzes. The prediction performance of AttentiveLearner (accuracy = 71.22%, kappa = 0.22) is comparable with existing research using dedicated sensors. AttentiveLearner has the potential to improve mobile learning by reducing the sensing equipment required by many state-of-the-art intelligent tutoring algorithms.
doi:10.1007/978-3-319-19773-9_37 fatcat:mupir2xqhvdtxc3fgyucl4qnke