PIs: Sidney D’Mello
University of Colorado at Boulder
The ability to concentrate on tasks is critical to learning. This project will develop attention-aware cyberlearning as a new genre of learning technologies that automatically detect and respond to students’ attentional states. In particular, this project will implement technology that will detect mind wandering (MW) which is when attention shifts from task-related thoughts to task-unrelated thoughts. MW has been studied in the context of complex comprehension tasks and it has been found that a high degree of MW leads to inferior performance. However MW has not been studied in the context of learning with technology and technology solutions have not been proposed to reduce MW. This project addresses MW in the context of learning with technology. The detection of MW is through the use of inexpensive eye-tracking devices. The devices will be integrated with software to detect MW while students are engaged in learning high school biology through an interactive system called Guru. Once MW is detected, software strategies will be used to enable the students return to the learning task. The primary research will be in the development and testing of MW detection algorithms and in the development and testing of strategies to reduce MW.
In more detail, the attention-aware Guru will include an integrated eye tracker, an automated gaze-based MW detector, and intervention strategies to improve learning by mitigating the costs of MW. The research will be conducted in 9th grade biology classrooms in Northern Indiana, where the core technological components will be formatively studied, iteratively refined, and summatively evaluated. Generalizable insights will be identified at every stage of the project in order to promote transferability of the findings to future attention-aware technologies, thereby helping students learn to their fullest potential. In summary, the proposed attention-aware Guru technology will be used to advance fundamental research focused on uncovering: (1) the incidence of MW during learning with technology, (2) relationships between MW and learning, (3) patterns of eye-gaze that are diagnostic of MW, (4) automated intervention strategies to reorient attention and reduce the detrimental effects of MW, and (5) generalizable insights to catalyze future implementations of attention-aware cyberlearning.