Multimodal affect detection in the wild: Accuracy, availability, and generalizability

Affect detection is an important component of computerized learning environments that adapt the interface and materials to students' affect. This paper proposes a plan for developing and testing multimodal affect detectors that generalize across differences in data that are likely to occur in practical applications (e.g., time, demographic variables). Facial features and interaction log features are considered as modalities for affect detection in this scenario, each with their own advantages. Results are presented for completed work evaluating the accuracy of individual modality faceand interaction-based detectors, accuracy and availability of a multimodal combination of these modalities, and initial steps toward generalization of face-based detectors. Additional data collection needed for cross-culture generalization testing is also completed. Challenges and possible solutions for proposed cross-cultural generalization testing of multimodal detectors are also discussed.