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Engagement is an essential indicator of the Quality-of-Learning Experience(QoLE) and plays a major role in developing intelligent educational interfaces.The number of people learning through Massively Open Online Courses (MOOCs) andother online resources has been increasing rapidly because they provide us withthe flexibility to learn from anywhere at any time. This provides a goodlearning experience for the students. However, such learning interface requiresthe ability to recognize the level of engagement of the students for a holisticlearning experience. This is useful for both students and educators alike.However, understanding engagement is a challenging task, because of itssubjectivity and ability to collect data. In this paper, we propose a varietyof models that have been trained on an open-source dataset of videoscreengrabs. Our non-deep learning models are based on the combination ofpopular algorithms such as Histogram of Oriented Gradient (HOG), Support VectorMachine (SVM), Scale Invariant Feature Transform (SIFT) and Speeded Up RobustFeatures (SURF). The deep learning methods include Densely ConnectedConvolutional Networks (DenseNet-121), Residual Network (ResNet-18) andMobileNetV1. We show the performance of each models using a variety of metricssuch as the Gini Index, Adjusted F-Measure (AGF), and Area Under receiveroperating characteristic Curve (AUC). We use various dimensionality reductiontechniques such as Principal Component Analysis (PCA) and t-DistributedStochastic Neighbor Embedding (t-SNE) to understand the distribution of data inthe feature sub-space. Our work will thereby assist the educators and studentsin obtaining a fruitful and efficient online learning experience.

DMCNet: Diversified Model Combination Network for Understanding Engagement from Video Screengrabs