An Ordinal Learning Framework for Modeling Perceived Indoor Environmental Quality Dissatisfaction in University Classrooms: A CORAL-Based Approach with Benchmark Model Evaluation

Indoor environmental quality (IEQ) strongly influences occupant well-being and teaching conditions in educational buildings. This study proposes the Predicted Probability of Dissatisfaction Index (PPDieq.prof), a probabilistic ordinal learning framework based on the Consistent Rank Logits (CORAL) architecture to estimate university professors’ dissatisfaction while preserving the ordinal structure of subjective perception. The framework integrates operative temperature, CO2 concentration, illuminance, and sound pressure level through the CORAL architecture. A dataset comprising 77 synchronized environmental and subjective observations collected during regular teaching activities was evaluated using a Nested Stratified Group Cross-Validation benchmark. The proposed framework achieved an Accuracy of 0.670±0.122, Balanced Accuracy of 0.715±0.164, Macro-F1 score of 0.618±0.166, and a Quadratic Weighted Cohen’s Kappa of 0.604±0.176. Benchmark comparisons with conventional statistical and machine learning models demonstrated competitive predictive performance while preserving ordinal consistency. The representative CORAL architecture selected during the benchmark was subsequently retrained using the complete dataset for model interpretability analyses. Predictor contribution analysis identified CO2 concentration as the most influential environmental variable (48.2%), followed by operative temperature (24.8%), sound pressure level (14.7%), and illuminance (12.4%). Complementary One-Factor-at-a-Time (OFAT) response analysis and exploratory latent-space visualization further enhanced the interpretation of environmental dissatisfaction, demonstrating the potential of the proposed framework as an interpretable virtual sensing approach for supporting IEQ assessment in educational buildings.