Qingdao Middle School Classroom Environmental Monitoring and Window-Opening Behavior Dataset

(2) All_Calculated_Data: 2,640 rows of aggregated statistical summaries (hourly and daily averages) with a 'Season' identifier column. (3–10) Season-specific sheets (Spring_Raw, Spring_Calculated, Summer_Raw, Summer_Calculated, Autumn_Raw, Autumn_Calculated, Winter_Raw, Winter_Calculated) preserving original file structure for reproducibility. This dataset supports machine learning-based analysis of window-opening behavior as a climate-resilient adaptive mechanism under institutionalized teaching schedules.

It has been used to train a Random Forest model (AUC-ROC = 0.897, F1-score = 0.749) with SHAP (SHapley Additive exPlanations) analysis to identify threshold-driven behavioral patterns across four daily activity phases: arrival, break, class, and leaving. Variable list (All_Raw_Data sheet): - Season: Spring / Summer / Autumn / Winter - Timestamp: Date and time (YYYY-MM-DD HH:MM:SS) - Indoor_Temperature_C: Indoor air temperature (°C) - Outdoor_Temperature_C: Outdoor air temperature (°C) - Indoor_CO2_ppm: Indoor CO₂ concentration (ppm) - Outdoor_CO2_ppm: Outdoor CO₂ concentration (ppm) - Indoor_PM2_5_ugm3: Indoor PM₂.₅ concentration (μg/m³) - Outdoor_PM2_5_ugm3: Outdoor PM₂.₅ concentration (μg/m³) - Indoor_PM10_ugm3: Indoor PM₁₀ concentration (μg/m³) - Outdoor_PM10_ugm3: Outdoor PM₁₀ concentration (μg/m³) - Indoor_RH_percent: Indoor relative humidity (%) - Outdoor_RH_percent: Outdoor relative humidity (%) - Window_Status: Window opening state (1 = open, 0 = closed)