Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students

Abstract

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.

12th Women in Machine Learning Workshop (WiML 2017), co-located with the 31st Conference on Neural Information Processing Systems (NeurIPS 2017), Long Beach, CA, USA