Stephen Hutt
Univeristy of Colorado Boulder, Boulder, Colorado, United States
We designed and tested an attention-aware learning technology (AALT) that detects and responds to mind wandering (MW), a shift in attention from task-related to task-unrelated thoughts, that is negatively associated with learning. We leveraged an existing gaze-based mind wandering detector that uses commercial off the shelf eye tracking to inform real-time interventions during learning with an Intelligent Tutoring System in real-world classrooms. The intervention strategies, co-designed with students and teachers, consisted of using student names, reiterating content, and asking questions, with the aim to reengage wandering minds and improve learning. After several rounds of iterative refinement, we tested our AALT in two classroom studies with 287 high-school students. We found that interventions successfully reoriented attention and, compared to two control conditions, reduced mind wandering and improved retention (measured via a delayed assessment) for students with low prior-knowledge who occasionally (but not excessively) mind wandered. We discuss implications for developing gaze-based AALTs for real-world contexts.
https://doi.org/10.1145/3411764.3445269
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)