Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students [article]

Nese Alyuz, Eda Okur, Utku Genc, Sinem Aslan, Cagri Tanriover, Asli Arslan Esme
2019 arXiv   pre-print
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.
arXiv:1901.05835v1 fatcat:73njb4q24vd4zlbhesyjxsbziq