Privacy-aware learning analytics for exploratory behavior through federated and explainable AI

Learning analytics platforms increasingly use behavioral traces to model engagement, navigation, and self-directed learning dynamics in virtual learning environments (VLEs). However, most current approaches prioritize predictive accuracy, centralize sensitive student records, and rarely analyze the stability of exploratory representations under distributed configurations. This study proposes a privacy-aware framework integrating behavioral learning analytics, federated learning (FedAvg), and Explainable AI to reconstruct informal and exploratory patterns from VLE traces while reducing direct exposure to raw data. Using OULAD as the primary dataset, the framework reconstructed 29,228 student-module-presentation units from 10,900,970 interactions and generated a behavioral proxy based on resource diversity, revisits, temporal dispersion, and non-evaluative activity. In the operational proxy-recoverability configuration, Logistic Regression achieved F1 = 0.977 and AUC-ROC = 1.000; this near-perfect value is interpreted as an upper-bound recovery of a transparent behavioral proxy rather than as external prediction of an independently observed construct. Importantly, the reduced scenario maintained F1 = 0.905 and AUC-ROC = 0.991 after excluding direct proxy components. Under distributed training, FedAvg preserved utility comparable to that of the centralized MLP (F1 = 0.671; AUC-ROC = 0.886), maintaining Overlap@10 = 1.000 and a Spearman correlation of ρ = 0.985 between centralized and federated explanatory rankings. The results suggest that exploratory representations can preserve analytical utility and explanatory stability in lightweight federated educational scenarios.