TY - GEN
T1 - Temporal generalizability of face-based affect detection in noisy classroom environments
AU - Bosch, Nigel
AU - D’Mello, Sidney
AU - Baker, Ryan
AU - Ocumpaugh, Jaclyn
AU - Shute, Valerie
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - The goal of this paper was to explore the possibility of generalizing face-based affect detectors across multiple days, a problem which plagues physiological- based affect detection. Videos of students playing an educational physics game were collected in a noisy computer-enabled classroom environment where students conversed with each other, moved around, and gestured. Trained observers provided real-time annotations of learning-centered affective states (e.g., boredom, confusion) as well as off-task behavior. Detectors were trained using data from one day and tested on data from different students on another day. These cross-day detectors demonstrated above chance classification accuracy with average Area Under the ROC Curve (AUC,.500 is chance level) of.658, which was similar to within-day (training and testing on data collected on the same day) AUC of.667. This work demonstrates the feasibility of generalizing face-based affect detectors across time in an ecologically valid computer-enabled classroom environment.
AB - The goal of this paper was to explore the possibility of generalizing face-based affect detectors across multiple days, a problem which plagues physiological- based affect detection. Videos of students playing an educational physics game were collected in a noisy computer-enabled classroom environment where students conversed with each other, moved around, and gestured. Trained observers provided real-time annotations of learning-centered affective states (e.g., boredom, confusion) as well as off-task behavior. Detectors were trained using data from one day and tested on data from different students on another day. These cross-day detectors demonstrated above chance classification accuracy with average Area Under the ROC Curve (AUC,.500 is chance level) of.658, which was similar to within-day (training and testing on data collected on the same day) AUC of.667. This work demonstrates the feasibility of generalizing face-based affect detectors across time in an ecologically valid computer-enabled classroom environment.
UR - https://www.scopus.com/pages/publications/84948958509
UR - https://www.scopus.com/pages/publications/84948958509#tab=citedBy
U2 - 10.1007/978-3-319-19773-9_5
DO - 10.1007/978-3-319-19773-9_5
M3 - Conference contribution
AN - SCOPUS:84948958509
SN - 9783319197722
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 44
EP - 53
BT - Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings
A2 - Conati, Cristina
A2 - Heffernan, Neil
A2 - Mitrovic, Antonija
A2 - Felisa Verdejo, M.
PB - Springer
T2 - 17th International Conference on Artificial Intelligence in Education, AIED 2015
Y2 - 22 June 2015 through 26 June 2015
ER -