Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students
This addresses the problem of monitoring student engagement unobtrusively for educators, but it appears incremental as it builds on existing multimodal methods.
The paper tackled detecting students' behavioral engagement (On-Task vs. Off-Task) using a multimodal approach based on appearance, context-performance, and mouse data, achieving results through decision-level fusion on a dataset from an authentic classroom.
We propose a multimodal approach for detection of students' behavioral engagement states (i.e., On-Task vs. Off-Task), based on three unobtrusive modalities: Appearance, Context-Performance, and Mouse. Final behavioral engagement states are achieved by fusing modality-specific classifiers at the decision level. Various experiments were conducted on a student dataset collected in an authentic classroom.