Explainable clustering and ranking of student satisfaction in online learning: a hybrid FAHP-TOPSIS and SHAP-LIME...

The rapid transition to online learning has intensified the need to understand the multifaceted drivers of student satisfaction in virtual education environments. Student satisfaction is a cornerstone of effective digital education, influencing academic performance, learner engagement, and institutional decision-making. As a critical predictor of retention and perceived instructional quality, it directly impacts the sustainability of online learning systems. However, existing studies often lack integrated frameworks that combine subjective expert judgment, global and local model interpretability, and subgroup-specific analysis. To address this gap, this study aims to develop a hybrid decision-analytic and explainable machine learning framework to systematically assess and interpret student satisfaction in virtual classrooms. The proposed framework adopts a layered analytical design in which statistical feature relevance (ANOVA), model-based interpretability (SHAP and LIME), and expert-driven weighting under uncertainty (FAHP) are treated as complementary but non-overlapping components, with TOPSIS used exclusively for decision-level satisfaction ranking. A structured survey has been conducted among 1,469 university students, capturing responses across 21 features encompassing demographic attributes, digital readiness, behavioral patterns, and perceived instructional quality. Feature selection has been performed using the ANOVA F-test, while SHapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) have been applied to derive global and local feature importance. To incorporate subjective expert judgment, feature weights have been calculated using the Fuzzy Analytic Hierarchy Process (FAHP), and composite satisfaction scores have been computed using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). These scores have been used to segment students via Agglomerative Hierarchical Clustering into low (n = 852), medium (n = 484), and high (n = 133) satisfaction cohorts. Cluster-specific SHAP and LIME analyses have uncovered distinct patterns of feature influence across subgroups. The identified satisfaction drivers align closely with constructs from established models of technology acceptance and learning motivation, including the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and Self-Determination Theory (SDT). High satisfaction has been linked to emotional positivity, perceived equivalence to offline instruction, and academic self-efficacy, whereas low satisfaction has been driven by affordability barriers, psychological stress, and misalignment with preferred learning modalities. The proposed framework offers a comprehensive, interpretable, and rank-aware approach to modeling student satisfaction, equipping educational stakeholders with actionable insights for designing more inclusive, equitable, and learner-centered online education environments.