EVA-LA: An Explainable Value-Added Learning Analytics Framework for Higher Education

Existing learning analytics approaches in higher education provide limited insight into students’ incremental academic growth and its underlying mechanisms. To address this gap, we propose an explainable value-added learning analytics framework, which contributes a residual-based value-added modeling paradigm, along with a cross-level interpretability mechanism for explaining student growth. It first constructs a structured learner representation that fuses heterogeneous digital trace data into a unified analytical space spanning behavioral, process, and performance dimensions. To quantify students’ academic growth, we build an elastic net-based estimator to model expected outcomes under high-dimensional and collinear predictors, enabling the derivation of individualized value-added scores as adjusted indicators of net learning progress. To enhance interpretability, we further develop Bi-Lens, a bidirectional explanatory framework that couples SHAP-based global attribution with LIME-based local diagnosis, supporting cross-level explanatory coherence and strengthening the identification of factors associated with value-added learning outcomes. Experiments on two real-world courses demonstrate competitive performance (R2 = 0.541 and 0.368) and stable, well-separated value-added estimates, while yielding interpretable insights for student development.