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Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students
January 16, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Nese Alyuz, Eda Okur, Utku Genc, Sinem Aslan, Cagri Tanriover, Asli Arslan Esme
arXiv ID
1901.05835
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.LG,
stat.ML
Citations
17
Venue
arXiv.org
Last Checked
4 months ago
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.
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