ArticleFrontiers in artificial intelligence2026
Privacy-aware learning analytics for exploratory behavior through federated and explainable AI.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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
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