AttentiveLearner: Improving Mobile MOOC Learning via Implicit Heart Rate Tracking

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, AttentiveLearner 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 24-participant 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.

[1]  Philip J. Guo,et al.  How video production affects student engagement: an empirical study of MOOC videos , 2014, L@S.

[2]  H. Fournier,et al.  New dimensions to self-directed learning in an open networked learning environment , 2012 .

[3]  Sidney K. D'Mello,et al.  Automated Physiological-Based Detection of Mind Wandering during Learning , 2014, Intelligent Tutoring Systems.

[4]  John F. Canny,et al.  Balancing Accuracy and Fun: Designing Camera Based Mobile Games for Implicit Heart Rate Monitoring , 2015, CHI.

[5]  J. Smallwood,et al.  The restless mind. , 2006, Psychological bulletin.

[6]  D. Gilbert,et al.  A Wandering Mind Is an Unhappy Mind , 2010, Science.

[7]  Bilge Mutlu,et al.  ARTFul: adaptive review technology for flipped learning , 2013, CHI.

[8]  Xiang Cao,et al.  L.IVE: an integrated interactive video-based learning environment , 2014, CHI.

[9]  Xiang Xiao,et al.  LensGesture: augmenting mobile interactions with back-of-device finger gestures , 2013, ICMI '13.

[10]  Beverly Park Woolf,et al.  Affect-aware tutors: recognising and responding to student affect , 2009, Int. J. Learn. Technol..

[11]  Alan Kingstone,et al.  Everyday attention: Mind wandering and computer use during lectures , 2013, Comput. Educ..

[12]  Rafael A. Calvo,et al.  Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications , 2010, IEEE Transactions on Affective Computing.

[13]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[14]  Joanna Drummond,et al.  In the Zone: Towards Detecting Student Zoning Out Using Supervised Machine Learning , 2010, Intelligent Tutoring Systems.

[15]  Sidney K. D'Mello,et al.  Toward Fully Automated Person-Independent Detection of Mind Wandering , 2014, UMAP.

[16]  Krzysztof Z. Gajos,et al.  Understanding in-video dropouts and interaction peaks inonline lecture videos , 2014, L@S.

[17]  Jodi Forlizzi,et al.  Psycho-physiological measures for assessing cognitive load , 2010, UbiComp.