Untangle the Effects of Classroom Environmental Features on Middle-School Students’ Mood Perception with Machine Learning and XAI

Proper daylighting in educational buildings improves students’ mood and health. However, daylighting can be affected by multiple environmental features, and comprehensive investigations remain limited. This study examined how various classroom environmental features affect students’ mood perception in six classrooms of three middle schools in eastern China. Eighteen environmental features across six dimensions were assessed through field studies and software simulations, and 557 valid mood responses were collected from 243 students through questionnaires. Traditional machine learning and deep learning models were used to predict students’ mood perception with the environmental features, with SHapley Additive exPlanations (SHAP) applied to interpret the contributions of different features. Results showed that Random Forest achieved a relatively high accuracy of 82% in the binary classification of mood perception prediction. Among all features, Exterior view evaluation (EVE) had the largest impact and showed a strong interaction with floor level. Higher floors and EVE ≥ 3 were associated with more positive moods. Beneficial conditions for mood perception also included horizontal desktop illuminance above 300 lx, frontal eye-level illuminance below 400 lx, left-side eye-level illuminance within 300–1000 lx, and proximity to windows below 2.5 m. These findings provide new insights and practical guidance for designing healthier classroom environments to promote adolescent mental health, thereby contributing to sustainable educational environments that integrate human well-being with energy-efficient daylighting design.