This track examines methods for collecting, analysing, and interpreting educational data to improve learning and teaching. It welcomes research on learning analytics, educational data mining, predictive modelling, knowledge tracing, learner profiling, and early-warning systems. Contributions may address multimodal data, temporal learning patterns, causal analysis, dashboards, and actionable visualisations. Particular attention is given to translating analytical results into effective pedagogical interventions. Studies should also consider data quality, privacy, fairness, explainability, and responsible use.

Focus: Data-driven methods that generate reliable and actionable insights into learning processes and outcomes. The track connects advanced analytics with interpretable evidence and effective educational decision-making.