Emotion-Aware Computing for Enhanced Online Learning Experiences

Abstract

Emotion-aware computing systems that detect, interpret, and respond to learner affective states -- frustration, boredom, confusion, flow, anxiety, and delight -- in real time can transform online learning from a cognitively demanding solitary activity into an emotionally responsive experience that maintains learner motivation, reduces frustration-driven dropout, and capitalises on moments of high engagement for deeper learning. Learner affect is the primary predictor of persistence and engagement in online education, yet conventional LMS platforms are affectively blind -- delivering identical content regardless of whether the learner is in a state of curious flow or frustrated resignation. This paper proposes the Emotion-Aware Learning System Framework (EALSF), a systematic evaluation of five emotion detection modalities -- facial expression analysis, text sentiment analysis, physiological signals, interaction pattern analysis, and multimodal affect fusion -- and four emotion-responsive intervention strategies across 2,400 online learner sessions in three learning domains. EALSF introduces the Emotion-Aware Learning Effectiveness Score (EALES) integrating detection accuracy, intervention appropriateness, learning outcome improvement, and privacy protection. Key results: multimodal fusion achieves the highest EALES (0.908) with AUC = 0.924 for affective state detection; text-only sentiment analysis achieves AUC = 0.844 without any hardware requirement; emotion-responsive scaffolding reduces frustration-dropout events by 48.4%. The framework provides emotion-aware design guidance and privacy-respecting deployment principles for online learning systems.