With the ubiquity of online learning, understanding the acoustic environment's impact on engagement in uncontrolled personal settings has become crucial. This study investigates the relationship between the qualitative (type) and quantitative (level) characteristics of noise and learner engagement in authentic online environments. Using over one million observations from 2,175 adult learners, we analyzed engagement states objectively measured via an AI computer vision model, employing Chi-square tests and Welch's ANOVA. Results revealed a significant association between noise type and engagement: ‘Silence’ was linked to an ‘Engaged-Calm’ state, while ‘Conversation’ correlated with a ‘Drowsy’ state. Paradoxically, the mean sound level during ‘engaged’ states was significantly higher than during ‘disengaged’ states. These findings suggest that an optimal learning environment is not merely quiet but a complex concept requiring consideration of both noise's informational characteristics and appropriate arousal levels. This empirical analysis offers significant academic and practical implications for designing future context-aware personalized learning systems. 

Keywords : Learning Engagement, Acoustic Environment, Noise Type, Online Learning, Computer Vision, Naturalistic Environment