Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students

no code implementations • 16 Jan 2019 • Nese Alyuz, Eda Okur, Utku Genc, Sinem Aslan, Cagri Tanriover, Asli Arslan Esme

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.

Detecting Behavioral Engagement of Students in the Wild Based on Contextual and Visual Data

no code implementations • 15 Jan 2019 • Eda Okur, Nese Alyuz, Sinem Aslan, Utku Genc, Cagri Tanriover, Asli Arslan Esme

To investigate the detection of students' behavioral engagement (On-Task vs. Off-Task), we propose a two-phase approach in this study.

The Importance of Socio-Cultural Differences for Annotating and Detecting the Affective States of Students

no code implementations • 12 Jan 2019 • Eda Okur, Sinem Aslan, Nese Alyuz, Asli Arslan Esme, Ryan S. Baker

One open question in annotating affective data for affect detection is whether the labelers (i. e., human experts) need to be socio-culturally similar to the students being labeled, as this impacts the cost feasibility of obtaining the labels.

Open-Ended Question Answering