  Predicting contextualised engagement in videos is a long-standing problem
that has been popularly attempted by exploiting the number of views or the
associated likes using different computational methods. The recent decade has
seen a boom in online learning resources, and during the pandemic, there has
been an exponential rise of online teaching videos without much quality
control. The quality of the content could be improved if the creators could get
constructive feedback on their content. Employing an army of domain expert
volunteers to provide feedback on the videos might not scale. As a result,
there has been a steep rise in developing computational methods to predict a
user engagement score that is indicative of some form of possible user
engagement, i.e., to what level a user would tend to engage with the content. A
drawback in current methods is that they model various features separately, in
a cascaded approach, that is prone to error propagation. Besides, most of them
do not provide crucial explanations on how the creator could improve their
content. In this paper, we have proposed a new unified model, CLUE for the
educational domain, which learns from the features extracted from freely
available public online teaching videos and provides explainable feedback on
the video along with a user engagement score. Given the complexity of the task,
our unified framework employs different pre-trained models working together as
an ensemble of classifiers. Our model exploits various multi-modal features to
model the complexity of language, context agnostic information, textual emotion
of the delivered content, animation, speaker's pitch and speech emotions. Under
a transfer learning setup, the overall model, in the unified space, is
fine-tuned for downstream applications.
