Explainable Learning Analytics for Modeling Emotional Engagement and Social Interaction Dynamics in Online Learning Communities
Keywords:
explainable learning analytics, emotional engagement, social interaction dynamics, online learning communities, system architecture, governance, fairness, robustnessAbstract
Online learning communities generate large volumes of interactional, textual, and temporal data that offer a basis for understanding how learners engage emotionally and socially. However, the dominant predictive orientation in learning analytics often produces models that are difficult for educators, learners, and institutional decision makers to interpret. This paper develops a systems-level account of explainable learning analytics for modeling two intertwined phenomena: emotional engagement and social interaction dynamics in online learning communities. It argues that explainability must be treated as a structural property of the learning analytics infrastructure rather than as a post hoc supplement to opaque prediction models. The discussion examines the conceptual foundations of emotional and social engagement, the architectural requirements for explainable modeling, and the trade-offs associated with local explanation and inherently interpretable models. The paper further addresses governance, fairness, deployment, sustainability, and robustness as integral components of such systems. Emotional engagement is conceptualized as a temporally varying process shaped by achievement emotions, while social interaction dynamics are treated as relational and network-based phenomena. The analysis emphasizes the need to align computational explanations with educational accountability mechanisms and institutional oversight. The paper closes with policy implications and future directions for building learning analytics infrastructures that are both technically rigorous and institutionally legitimate.
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