AttentiveLearner includes a server, visualization interfaces for instructors, and apps for major mobile platforms (e.g. iPhone, iPad, Android). AttentiveLearner is free for Massive Open Online Courses (MOOCs) and flipped classrooms.

The AttentiveLearner mobile client has four unique components when compared with today’s MOOC mobile apps: 1) a tangible video control channel; 2) an implicit heart rate sensing module, 3) an on-screen AttentiveWidget visualizing real-time states of video control and heart rate sensing; and 4) algorithms that infers learners’ physiological states, cognitive states, and major learning events from physiological signals and contextual signals captured.

AttentiveLearner can be fully integrated into a MOOC platform. Follow are some screenshots of AttentiveLearner on a Nexus 5X smartphone in common usage scenarios.


 Fig 1. Getting started: (a) welcome screen; (b) register a new account; (c) login with existing account.



Fig 2. Enrolling and browsing courses: (a) select an enrolled course; (b) viewing course information; (c) select a tutorial video to study.


Fig 3. Learning MOOC courses (normal view): (a) cover the back camera lens to play the video; (b) uncover the lens to pause the video.


Fig 4. Watching tutorial video (advanced view): (a) cover the back camera lens to play the video, and extracting learner’s physiological features; (b) uncover the lens to pause the video, and stop extracting learner’s physiological features

There are several widgets come along with AttentiveLearner (Fig. 4). The Camera View widget shows the back camera lens’ view in real time, e.g. the red color in Fig. 4 is the real skin color of the covering fingertip under the phone’s flash light. The Attentive Indicator widget notifies the lens covering status which is directly related to the physiological signal recording process. PPG Signal indicator shows real time Photoplethysmogram (PPG) signal recorded implicitly from the covering fingertip. Last but not least, Extracted Features widget shows, in real time, extracted features used to infer learner’s cognitive states. With these functionalities, AttentiveLearner can be used to infer implicitly learner’s cognitive states while watching tutorial videos. Thus, AttentiveLearner can provide a bi-directional and attentive learning MOOC environment on unmodified smartphones without dedicated hardware.