Real-Time Emotion Recognition in Online Learning Using Google Teachable

Understanding learners’ emotional engagement in e-learning environments remains challenging due to the limited availability of non-verbal cues, despite its importance for motivation and participation. This paper proposes a facial emotion recognition approach using Google's Teachable Machine to support real-time emotion detection within online learning environments. The system analyzes facial expressions captured through a standard webcam to classify four basic emotional states: happy, sad, neutral, and angry. An experimental design was employed using simulated emotional expressions collected under controlled conditions, including adequate lighting and front-facing facial images. The results indicate that the system can provide instructors with additional affective cues to support formative assessment and instructional awareness in synchronous online learning. The proposed approach emphasizes practical instructional feasibility and accessibility compared to more complex emotion recognition models, as it does not require specialized hardware or advanced programming skills.