TY - GEN
T1 - Out of the Fr-"Eye"-ing Pan
T2 - 25th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017
AU - Hutt, Stephen
AU - Mills, Caitlin
AU - Bosch, Nigel
AU - Krasich, Kristina
AU - Brockmole, James
AU - D'mello, Sidney
N1 - Funding Information:
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
Publisher Copyright:
©2017 ACM.
PY - 2017/7/9
Y1 - 2017/7/9
N2 - 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.
AB - 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.
KW - Attention-Aware learning
KW - Cyberlearning
KW - Eye-gaze
KW - Intelligent tutoring systems
KW - Mind wandering
UR - http://www.scopus.com/inward/record.url?scp=85026756708&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85026756708&partnerID=8YFLogxK
U2 - 10.1145/3079628.3079669
DO - 10.1145/3079628.3079669
M3 - Conference contribution
AN - SCOPUS:85026756708
T3 - UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization
SP - 94
EP - 103
BT - UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization
PB - Association for Computing Machinery, Inc
Y2 - 9 July 2017 through 12 July 2017
ER -