Multi-label disengagement and behavior prediction in online learning

Abstract

Student disengagement prediction in online learning environments is beneficial in various ways, especially to help provide timely cues to make some feedback or stimuli to the students. In this work, we propose a neural network-based model to predict students’ disengagement, as well as other behavioral cues, which might be relevant to students’ performance, using facial image sequences. For training and evaluating our model, we collected samples from multiple participants and annotated them with temporal segments of disengagement and other relevant behavioral cues with our multiple in-house annotators. We present prediction results of all behavior cues along with baseline comparison.

Manisha Verma

Specially-Appointed Researcher/Fellow

Manisha’s research interest broadly lies in computer vision and image processing. Currently, she is working on micro facial expression recognition using multi-model deep learning frameworks.

Yuta Nakashima

Professor

Yuta Nakashima is a professor with Institute for Datability Science, Osaka University. His research interests include computer vision, pattern recognition, natural langauge processing, and their applications.

Noriko Takemura

Guest Associate Professor

She is working on ambient intelligence and gait recognition using pattern recognition and machine learning.

Hajime Nagahara

Professor

He is working on computer vision and pattern recognition. His main research interests lie in image/video recognition and understanding, as well as applications of natural language processing techniques.