Online Privacy-Safe Engagement Tracking System
Cheng Zhang, Cheng Chang, Lei Chen, Yang Liu · 2018
Tracking learners' engagement is useful for monitoring their learning quality. With an increasing number of online video courses, a system that can automatically track learners' engagement is expected to significantly help in improving the outcomes of learners' study. In this demo, we show such a system to predict a user's engagement changes in real time. Our system utilizes webcams ubiquitously existing in nowadays computers, the face tracking function that runs inside the Web browsers to avoid sending learners' videos to the cloud, and a Python Flask web service. Our demo provides a solution of using mature technologies to provide real-time engagement monitoring with privacy protection.