Engagement Analysis of Students in Online Learning Environments

Abstract

Engagement rate is considered a metric that measures the extent of engagement a particular content is receiving from the audience. In e-learning settings, educators want to observe the level of interest of learners to appropriately modify their courses and make the educational process more effective. In this paper, an ensemble approach is proposed to detect student engagement levels while watching an e-learning video. The ensemble model consists of a deep convolutional neural network (DCNN) for facial expression recognition and a deep recurrent neural network (DRNN) for establishing a relationship between eye-gaze and engagement intensity. OpenFace 2.0 toolbox abilities are leveraged for feature extraction. Experimental results on the test datasets give an accuracy of 55.64% on DAiSEE and an MSE of 0.0598 on Engagement in the Wild Dataset. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.