Learning Engagement Assessment in MOOC Scenario

Abstract

Engagement recognition is essential for monitoring online learning for efficient learning outcomes. By monitoring the student's engagement, the teacher will acquire timely feedback, diminish the dropout rates, and overcome educational problems. A novel Facial Engagement Analysis-Network (FEA-Net) is proposed for learning engagement assessment in Massive Open Online Courses (MOOC) scenarios. In a MOOC setting, the combination of spatio-temporal and OpenFace features fed into FEA-Net proved effective for classifying engagement levels. The proposed FEA-Net built using Depthwise Separable Convolution layer helped improve the system's performance by reducing the model complexity. The experiment results showed an improvement of 1.01% in terms of accuracy on the Dataset for Affective States in E-learning Environments (DAiSEE) dataset. © 2022 IEEE.