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Predicting contextualised engagement in videos is a long-standing problemthat has been popularly attempted by exploiting the number of views or theassociated likes using different computational methods. The recent decade hasseen a boom in online learning resources, and during the pandemic, there hasbeen an exponential rise of online teaching videos without much qualitycontrol. The quality of the content could be improved if the creators could getconstructive feedback on their content. Employing an army of domain expertvolunteers to provide feedback on the videos might not scale. As a result,there has been a steep rise in developing computational methods to predict auser engagement score that is indicative of some form of possible userengagement, i.e., to what level a user would tend to engage with the content. Adrawback in current methods is that they model various features separately, ina cascaded approach, that is prone to error propagation. Besides, most of themdo not provide crucial explanations on how the creator could improve theircontent. In this paper, we have proposed a new unified model, CLUE for theeducational domain, which learns from the features extracted from freelyavailable public online teaching videos and provides explainable feedback onthe video along with a user engagement score. Given the complexity of the task,our unified framework employs different pre-trained models working together asan ensemble of classifiers. Our model exploits various multi-modal features tomodel the complexity of language, context agnostic information, textual emotionof the delivered content, animation, speaker's pitch and speech emotions. Undera transfer learning setup, the overall model, in the unified space, isfine-tuned for downstream applications.

CLUE: Contextualised Unified Explainable Learning of User Engagement in Video Lectures