Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students

Nese Alyuz; Eda Okur; Utku Genc; Sinem Aslan; Cagri Tanriover; Asli Arslan Esme

Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students

Human-Computer Interaction 2019-01-18 v1 Machine Learning Machine Learning

Authors: Nese Alyuz , Eda Okur , Utku Genc , Sinem Aslan , Cagri Tanriover , Asli Arslan Esme

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.

Keywords

behavioral engagement multimodal approach unobtrusive modalities student dataset classifier fusion

Cite

@article{arxiv.1901.05835,
  title  = {Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students},
  author = {Nese Alyuz and Eda Okur and Utku Genc and Sinem Aslan and Cagri Tanriover and Asli Arslan Esme},
  journal= {arXiv preprint arXiv:1901.05835},
  year   = {2019}
}

Comments

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

Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students

Abstract

Keywords

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