Attention Network for Engagement Prediction in the Wild

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 lab-controlled 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.