Continual Learning for Human–AI Collaborative Learning Analytics under Behavioral Drift

Abstract

Semester-to-semester non-stationarity undermines the reliability of adaptive learning analytics, particularly when predictive models are deployed without explicit drift monitoring and controlled updating. This study analyzes a 14-semester longitudinal panel constructed from learning management system traces and assessment records, covering 18–21 distinct courses per semester and 812–936 active students per term. Drift is concentrated in performance-relevant behavioral channels, with the strongest intensity observed in practice attempts, submission timeliness, and session regularity, alongside a pronounced regime shift around the mid-sequence semester. Under semester-forward evaluation, a static model yields mean macro-F1 of 0.706 with a worst-semester macro-F1 of 0.652 and high volatility across semesters (std 0.030). Periodic retraining improves mean macro-F1 to 0.724 and worst-semester macro-F1 to 0.681 (std 0.022) but remains sensitive to update timing. Drift-aware continual learning achieves the highest and most stable performance, improving mean macro-F1 to 0.742 and worst-semester macro-F1 to 0.711 while reducing temporal variance (std 0.015) and increasing mean AUROC to 0.812. Reliability gains are reflected in lower expected calibration error (ECE 0.039 versus 0.056 for static) and improved decision quality at fixed intervention capacity, raising risk precision from 0.62 to 0.69 and risk recall from 0.48 to 0.56 while reducing alert volatility (CV 0.14 versus 0.29). Fairness robustness improves under drift-aware updating, reducing mean subgroup recall gap from 0.118 to 0.082 and lowering the maximum recall gap from 0.172 to 0.121. Ablation shows that intermediate drift thresholds balance robustness and governance load, sustaining worst-semester performance with approximately 1–2 updates per semester and diminishing returns beyond moderate replay memory.