Towards emotion-aware online learning through facial expression analysis

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