Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students

2019/01/16 by Alyuz, Nese, Okur, Eda, Genc, Utku +3 · 1 citation
#FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · doi:10.48550/arxiv.1901.05835

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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