Abstract:
Digital revolution has transformed the traditional
teaching procedures, students are going online to access study
materials. It is realised that analysis of student engagement
in an e-learning environment would facilitate effective task
accomplishment and learning. Well known social cues of engagement/disengagement can be inferred from facial expressions,
body movements and gaze patterns. In this paper, student’s
response to various stimuli (educational videos) are recorded and
cues are extracted to estimate variations in engagement level. We
study the association of a subject’s behavioral cues with his/her
engagement level, as annotated by labelers. We have localized
engaging/non-engaging parts in the stimuli videos using a deep
multiple instance learning based framework, which can give useful insight into designing Massive Open Online Courses (MOOCs)
video material. Recognizing the lack of any publicly available
dataset in the domain of user engagement, a new ‘in the wild’
dataset is curated. The dataset: Engagement in the Wild contains
264 videos captured from 91 subjects, which is approximately
16.5 hours of recording. Detailed baseline results using different
classifiers ranging from traditional machine learning to deep
learning based approaches are evaluated on the database. Subject
independent analysis is performed and the task of engagement
prediction is modeled as a weakly supervised learning problem.
The dataset is manually annotated by different labelers and
the correlation studies between annotated and predicted labels
of videos by different classifiers are reported. This dataset
creation is an effort to facilitate research in various e-learning
environments such as intelligent tutoring systems, MOOCs, and
others.
