Temporal Generalizability Of Face-Based Affect Detection In Noisy Classroom Environments

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.