BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Chicago
X-LIC-LOCATION:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20251016T135137Z
LOCATION:Grand A
DTSTART;TZID=America/Chicago:20251015T154000
DTEND;TZID=America/Chicago:20251015T160000
UID:HFESAM_ASPIRE 2025_sess220_LECT204@linklings.com
SUMMARY:Can We See Team Situation Awareness? Predicting and Classifying Te
 am SA Using Eye Tracking and Machine Learning
DESCRIPTION:Jad Atweh (University of Virginia, University of Florida) and 
 Sara Riggs (University of Virginia)\n\nTeam Situation Awareness (TSA) is c
 ritical for effective decision-making in complex domains, but it is diffic
 ult to measure and predict in real time. This study explores whether overa
 ll TSA and its three levels (i.e., perception, comprehension, and projecti
 on) can be predicted and classified using machine learning and eye trackin
 g data. Data was collected from 35 UAV teams (70 participants total) perfo
 rming multitasking scenarios while eye tracking data was recorded and SAGA
 T data was collected. Multiple regression and classification models were t
 rained using seven eye tracking metrics. Results showed that kNN and XGBoo
 st classifiers achieved strong accuracy across TSA levels, with up to 90% 
 accuracy and 0.95 F1-score for comprehension-level TSA. Predictive models 
 yielded moderate R² values, suggesting the need for further refinement. Th
 ese findings demonstrate the feasibility of using eye tracking and machine
  learning to model TSA in real time, laying the foundation for adaptive de
 cision-support systems that monitor and respond to TSA breakdowns in high-
 risk environments.\n\nTrack: Cognitive Engineering & Decision Making, Huma
 n AI Robot Teaming (AI)\n\nSession Chairs: Changwon Son (Texas Tech Univer
 sity) and Ekim Koca (University of Virginia)\n\n
END:VEVENT
END:VCALENDAR