Towards emotion-aware online learning through facial expression analysis
International Journal of Intelligent Computing and Cybernetics, ss.1-13, 2026 (ESCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1108/ijicc-04-2026-0372
- Dergi Adı: International Journal of Intelligent Computing and Cybernetics
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, ABI/INFORM, Aerospace Database, Compendex, INSPEC, zbMATH, Technology Collection (ProQuest)
- Sayfa Sayıları: ss.1-13
- Anahtar Kelimeler: Affective computing, Educational artificial intelligence, Emotion-aware learning, Facial expression analysis, Learning analytics, Online learning
- İstanbul Kültür Üniversitesi Adresli: Evet
Özet
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 – Reframes facial expression recognition as an affective sensing problem for emotion-aware online learning. Proposes a label-informed pipeline linking self-reported learner emotions with facial-expression-based affective cues. Provides an end-to-end framework for processing authentic webcam data collected during online learning sessions. Shows that lightweight and pretrained CNN models can extract broad affective cues under a unified three-class taxonomy.