Abstract:
Analysis of the student engagement in an e-learning environment
would facilitate effective task accomplishment and learning. Generally,
engagement/disengagement can be estimated from facial
expressions, body movements and gaze pattern. The focus of this
Ph.D. work is to explore automatic student engagement assessment
while watching Massive Open Online Courses (MOOCs) video material
in the real-world environment. Most of the work till now
in this area has been focusing on engagement assessment in labcontrolled
environments. There are several challenges involved in
moving from lab-controlled environments to real-world scenarios
such as face tracking, illumination, occlusion, and context. The
early work in this Ph.D. project explores the student engagement
while watching MOOCs. The unavailability of any publicly available
dataset in the domain of user engagement motivates to collect
dataset in this direction. The dataset contains 195 videos captured
from 78 subjects which are about 16.5 hours of recording. This
dataset is independently annotated by different labelers and final
label is derived from the statistical analysis of the individual labels
given by the different annotators. Various traditional machine
learning algorithm and deep learning based networks are used to
derive baseline of the dataset. Engagement prediction and localization
are modeled as Multi-Instance Learning (MIL) problem. In this
work, the importance of Hierarchical Attention Network (HAN) is
studied. This architecture is motivated from the hierarchical nature
of the problem where a video is made up of segments and segments
are made up of frames.
