Student Engagement Detection: Case Study on Using Peer-to-Peer Emotion Comparison with Context Regularization

Abstract

This paper describes a method to automatically assess participants' engagement in online education. Similar to emotion recognition, student engagement can be subjective. Hence, it is challenging to obtain large-enough and consistent ground-truth engagement labels for automatic student engagement. We propose an unsupervised method that could detect abnormal engagement states using peer-to-peer emotion correlation analysis in different modalities. Without any human engagement labeling, this zero-shot method accurately pinpoints the abnormal student engagements in our experiment. Modality-dependent engagement prediction also suggests possible distractions on the student's device.