Decoding Seating Preferences in Study Spaces via Explainable Machine Learning: Uncovering Micro-Scale Environment-Behavior Patterns Through the Lens of Gender and Efficiency

Seat selection in learning spaces intuitively reflects user preferences for micro-environments and facilities. To address the lack of integrated analysis of multi-dimensional factors in existing research, this study constructs a framework merging multi-source dynamic sensing with explainable machine learning (XGBoost/SHAP/GAM) to decode the non-linear environment–behavior mechanisms underlying seat selection in study rooms. A multi-source dataset was constructed using YOLOv8 for non-intrusive extraction of seat occupancy and user attributes (gender and learning efficiency), combined with Ladybug-based luminous–thermal simulations and spatial topological measurements. The results indicate that: (1) key environmental variables exhibit distinct comfort thresholds, with an optimal illuminance of 400–600 lx and an effective attraction radius for power sockets of 1.5–3.0 m; (2) high-efficiency learners are highly sensitive to path interference, exhibiting a prominent “defensive” seat selection strategy; (3) significant divergences exist between genders regarding spatial depth preferences; and (4) compensatory and synergistic effects exist among multi-dimensional factors, where peak occupancy or superior lighting enhances user tolerance for the absence of sockets. This study quantifies the non-linear interactions between micro-physical environments and spatial behavior, providing a direct data-driven basis for refined facility deployment, dynamic luminous–thermal interventions, and scientific dynamic–static zoning in future learning spaces.