Explainable and Fair Student Dropout Prediction from Multimodal Temporal Learning Analytics

We propose a multimodal temporal framework that integrates demographic, academic, and week-by-week VLE behavioral data for early student dropout prediction.
We develop a dual-input BiLSTM architecture that jointly models static student characteristics and temporal learning engagement patterns to improve predictive performance.
We incorporate SHAP and LIME to provide both global and student-level explanations, enabling educators to understand the key factors underlying dropout predictions.
We conduct a systematic fairness assessment across gender, age, disability status, and socio-economic deprivation to identify potential disparities in model performance and prediction errors.
We translate predictive and explainability outputs into risk levels and personalized intervention recommendations, supporting actionable early-warning decision-making in virtual learning environments.