Characterizing Learners' Complex Attentional States During Online Multimedia Learning Using Eye-Tracking, Egocentric Camera, Webcam, and Retrospective Recalls

As online learning becomes increasingly ubiquitous, a key challenge is maintaining learners' sustained attention. Using eye-tracking, together with observing and interviewing learners, we can characterize both 1) whether they are looking at their learning materials, and 2) whether they are thinking about them. Critically, eye-tracking only speaks to the first distinction, not the second. To overcome this limitation, we supplemented eye-tracking with an egocentric camera, a webcam, a retrospective recall, and mind-wandering probes to capture a 2x2 matrix of attentional/cognitive states. We then categorized N=101 learners' attentional/cognitive states while they completed a multimedia physics module. This meets two goals: 1) allowing basic research to understand the relationship between attentional/cognitive states and behavioral outcomes; and 2) facilitating applied research by generating rich ground truth for future use in training machine learning to categorize this 2x2 set of attentional states, for which eye-tracking is necessary, but not sufficient.