Xiang Xiao
Jingtao Wang
ABSTRACT
We present Context and Cognitive State triggered Feed-Forward (C2F2), an intelligent tutoring system and algorithm, to improve both student engagement and learning efficacy in mobile Massive Open Online Courses (MOOCs). C2F2 infers and responds to learners' boredom and disengagement events in real time via a combination of camera-based photoplethysmography (PPG) sensing and learning topic importance monitoring. It proactively reminds a learner of upcoming important content (feed-forward interventions) when disengagement is detected. C2F2 runs on unmodified smartphones and is compatible with courses offered by major MOOC providers. In a 48-participant user study, we found that C2F2 on average improved learning gains by 20.2% when compared with a baseline system without the feed-forward intervention. C2F2 was especially effective for the bottom performers and improved their learning gains by 41.6%. This study demonstrates the feasibility and potential of using the PPG signals implicitly recorded by the built-in camera of smartphones to facilitate mobile MOOC learning.
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Index Terms
- Context and cognitive state triggered interventions for mobile MOOC learning
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