Attention-aware Cyberlearning


This project advances attention-aware cyberlearning as a new genre of learning technologies that automatically detect and respond to students’ attentional states. The exemplary implementation targets attentional lapses in the form of mind wandering (MW), a ubiquitous phenomenon where attention drifts away from task-related thoughts to task-unrelated thoughts. Considerable research has indicated that MW is highly frequent during complex comprehension tasks and is negatively correlated with performance outcomes. However, there is a dearth of systematic research on the incidence and impact of MW during learning with technology and technological solutions to combat MW have not yet been considered.

To address this, the proposed project blends the scientific goal of advancing basic research on MW during cyberlearning with the design goal of developing an attention-aware learning technology aimed at catching and combating wandering minds. Our exemplary technological innovation leverages emerging consumer-grade eye tracking (available as low as $99 per unit) and recent advances in mental state estimation to add an attention-aware computing layer to Guru, a cyberlearning technology for high-school biology. 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.

Recommended Reading

Press Releases

Research Team & Collaborators

  • Myrthe Faber (postdoc - Psychology)

  • Caitlin Mills (graduate student - Psychology)

    • Phillip (Nigel) Bosch (graduate student - Computer Science)

    • Kristina Krasich (graduate student - Psychology)

    • Robert Bixler (graduate student - Computer Science)

  • Angela Stewart (graduate student - Computer Science)

    • Stephen Hutt (graduate student - Computer Science

  • Shelby White (research coordinator)

  • Disha Waghray (undergraduate research assistant)

    • John Gensic (Teacher consultant)

    • Matt Kloser (Faculty Advisor)


This research was supported by the National Science Foundation (NSF) (IIS 1523091). Any opinions, findings and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the NSF.