Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students

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Nese Alyüz, Eda Okur, Utku Genc, Sinem Aslan, Cagri Tanriover, Asli Arslan Esme

Published: 2019, Last Modified: 16 Nov 2025CoRR 2019EveryoneRevisionsBibTeXCC BY-SA 4.0
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.