Towards emotion-aware online learning through facial expression analysis

Abstract

Purpose Online learning environments provide rich digital traces of learner activity, yet they offer limited access to the non-verbal affective cues that instructors naturally observe in face-to-face classrooms. This study investigates facial expression analysis as an affective sensing component for emotion-aware online learning. Design/methodology/approach Facial data were collected from 26 students during online learning sessions, and self-reported emotion labels were mapped into three affective categories: positive, neutral and negative. The visual stream was transformed into standardized face-centered representations and evaluated using a lightweight CNN implementation together with representative pretrained CNN architectures. Findings The results show that facial expressions are perceived by participants as meaningful non-verbal cues in online learning. In the classification experiments, VGG19 achieved the highest accuracy (0.79), while the lightweight CNN achieved a comparable accuracy (0.78) with the lowest loss value. Originality/value These findings suggest that facial-expression-based affective cues can be extracted from online learning data and may complement conventional learning analytics in future emotion-aware educational systems. Highlights