Xiang Xiao
Jingtao Wang
ABSTRACT
The emergence of mobile apps for Massive Open Online Courses (MOOCs) allows learners to access quality learning materials at low cost and "to control where, what, how and with whom they learn". Unfortunately, when compared with traditional classroom education, learners face more distractions and are more likely to multitask when they study alone in an informal learning environment. In this paper, we investigate the impact of divided attention (DA) on both the learning process and learning outcomes in the context of mobile MOOC learning. We propose OneMind, a system and algorithm for detecting divided attention on unmodified mobile phones via implicit, camera-based heart rate tracking. In an 18-participant study, we found that internal divided attention has a significant negative impact on learning outcomes; and that the photoplethysmography (PPG) waveforms implicitly captured by OneMind can be used to detect the presence, type, and intensity of divided attention in mobile MOOC learning.
Supplemental Material
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Index Terms
- Understanding and Detecting Divided Attention in Mobile MOOC Learning
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