@inproceedings{c1c716deaca14c42bde51c8fdfb6c017,
title = "Out of the Fr-{"}Eye{"}-ing Pan: Towards gaze-based models of attention during learning with technology in the classroom",
abstract = "Attention is critical to learning. Hence, advanced learning technologies should benefit from mechanisms to monitor and respond to learners' attentional states. We study the feasibility of integrating commercial off-The-shelf (COTS) eye trackers to monitor attention during interactions with a learning technology called GuruTutor. We tested our implementation on 135 students in a noisy computer-enabled high school classroom and were able to collect a median 95\% valid eye gaze data in 85\% of the sessions where gaze data was successfully recorded. Machine learning methods were employed to develop automated detectors of mind wandering (MW) -A phenomenon involving a shift in attention from task-related to task-unrelated thoughts that is negatively correlated with performance. Our student-independent, gaze-based models could detect MW with an accuracy (F1 of MW = 0.59) significantly greater than chance (F1 of MW = 0.24). Predicted rates of mind wandering were negatively related to posttest performance, providing evidence for the predictive validity of the detector. We discuss next steps towards developing gaze-based, attention-Aware, learning technologies that can be deployed in noisy, real-world environments.",
keywords = "Attention-Aware learning, Cyberlearning, Eye-gaze, Intelligent tutoring systems, Mind wandering",
author = "Stephen Hutt and Caitlin Mills and Nigel Bosch and Kristina Krasich and James Brockmole and Sidney D'mello",
note = "This research was supported by the National Science Foundation (NSF) (DRL 1235958 and 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; 25th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017 ; Conference date: 09-07-2017 Through 12-07-2017",
year = "2017",
month = jul,
day = "9",
doi = "10.1145/3079628.3079669",
language = "English (US)",
series = "UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization",
publisher = "Association for Computing Machinery",
pages = "94--103",
booktitle = "UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization",
address = "United States",
}