Fine-grained Engagement Recognition in Online Learning Environment

Abstract

Engagement is an important measure of users learning experience in online learning environment. Improving the accuracy of engagement recognition can help the instructors get timely feedback on the courses, optimize the recommendation strategies of online learning platforms and enhance users learning experience. In this paper, we propose a novel model: Deep Engagement Recognition Network (DERN) which combines temporal convolution, bidirectional LSTM and attention mechanism to identify the degree of engagement based on the features captured by OpenFace. In order to verify the validity and stability of the model, we evaluate the accuracy by the way of five-fold cross-validation. Finally, we achieved 60% in top-1 accuracy in the problem of four classification for engagement on the dataset called DAISEE which provided a baseline of 57.9%.