Abstract
Mind wandering, defined as shifts in attention from task-related processing to task-unrelated thoughts, is a ubiquitous phenomenon that has a negative influence on performance and productivity in many contexts, including learning. We propose that next-generation learning technologies should have some mechanism to detect and respond to mind wandering in real-time. Towards this end, we developed a technology that automatically detects mind wandering from eye-gaze during learning from instructional texts. When mind wandering is detected, the technology intervenes by posing just-in-time questions and encouraging re-reading as needed. After multiple rounds of iterative refinement, we summatively compared the technology to a yoked-control in an experiment with 104 participants. The key dependent variable was performance on a post-reading comprehension assessment. Our results suggest that the technology was successful in correcting comprehension deficits attributed to mind wandering (d = .47 sigma) under specific conditions, thereby highlighting the potential to improve learning by “attending to attention.”
Original language | English (US) |
---|---|
Pages | 8-15 |
Number of pages | 8 |
State | Published - 2017 |
Externally published | Yes |
Event | 10th International Conference on Educational Data Mining, EDM 2017 - Wuhan, China Duration: Jun 25 2017 → Jun 28 2017 |
Conference
Conference | 10th International Conference on Educational Data Mining, EDM 2017 |
---|---|
Country/Territory | China |
City | Wuhan |
Period | 6/25/17 → 6/28/17 |
Keywords
- Attention-aware
- Gaze tracking
- Mind wandering
- Student modeling
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems