  In this paper, we introduce a new dataset for student engagement detection
and localization. Digital revolution has transformed the traditional teaching
procedure and a result analysis of the 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 pattern. In this paper, student's response
to various stimuli videos are recorded and important cues are extracted to
estimate variations in engagement level. In this paper, we study the
association of a subject's behavioral cues with his/her engagement level, as
annotated by labelers. We then localize 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 created to study the
subject engagement problem. The dataset contains 195 videos captured from 78
subjects which is about 16.5 hours of recording. We present detailed baseline
results using different classifiers ranging from traditional machine learning
to deep learning based approaches. The subject independent analysis is
performed so that it can be generalized to new users. The problem of engagement
prediction is modeled as a weakly supervised learning problem. The dataset is
manually annotated by different labelers for four levels of engagement
independently and the correlation studies between annotated and predicted
labels of videos by different classifiers is reported. This dataset creation is
an effort to facilitate research in various e-learning environments such as
intelligent tutoring systems, MOOCs, and others.
