Federated and explainable learning analytics for privacy-preserving academic risk modeling across heterogeneous educational institutions

The increasing digitization of higher education has enabled the development of learning analytics models to identify students at risk of academic failure or dropout; however, most existing approaches rely on centralized training and assume homogeneous data distributions, limiting their applicability across institutions with heterogeneous student populations and interaction patterns, while privacy constraints restrict data sharing and hinder collaborative model development. To address these challenges, this study proposes a federated, explainable learning analytics framework for modeling academic risk trajectories under controlled institutional heterogeneity. The proposed architecture integrates temporal behavioral representations with socio-academic features within a multitask learning scheme, evaluated under both centralized and federated regimes, while modeling institutional heterogeneity through parameterized non-IID partitions that introduce controlled class imbalance, temporal drift, and feature-level variability. Experimental results show that federated models preserve strong discriminative performance and stable convergence as heterogeneity increases. At the same time, calibration metrics exhibit greater sensitivity to distributional shifts, revealing a decoupling between ranking performance and probabilistic reliability. In parallel, explainability analysis shows that the relative importance of features remains structurally stable across institutions, despite variations in contribution magnitudes. Cross-platform evaluation further shows that models retain discriminative capacity when transferred across educational environments, while exhibiting changes in calibration and explanatory intensity. These findings highlight the importance of multidimensional evaluation in federated learning systems, jointly considering performance, calibration, and interpretability, and provide a methodological framework for deploying robust and privacy-preserving learning analytics models in heterogeneous educational settings.