Real-Time Monitoring of Item Parameter Drift in CD-CAT via Adaptive CUSUM Charts

Item parameter drift (IPD) poses a persistent threat to measurement validity in computerized adaptive testing (CAT).While CUSUM-based online calibration methods have shown promise for real-time IPD monitoring within the item response theory (IRT) framework, their application to cognitive diagnostic CAT (CD-CAT) remains limited.Moreover, conventional psychometric implementations of CUSUM control charts often rely on a fixed reference value, which lacks adaptability to dynamic testing conditions and whose optimal magnitude is typically unknown in practice.To address these limitations, this study introduces adaptive CUSUM charts that dynamically update the drift magnitude using exponentially weighted least squares (EWLS) and exponentially weighted moving average (EWMA).Both univariate and multivariate variants are developed within a unified CD-CAT online calibration and monitoring framework.Two simulation studies and an empirical application to the LASSO Mechanics Cognitive Diagnostic dataset evaluate the proposed framework.Results indicated that all CUSUM charts outperformed the LRT in controlling false alarms.Among CUSUM variants, Downloaded from https://www.cambridge.org/core.04 Aug 2026 at 19:55:00, subject to the Cambridge Core terms of use.adaptive CUSUMs provided more conservative signalling for smaller IPD size and noisier residuals, while maintaining strong performance under abrupt drift, the dominant pattern in CD-CAT.Multivariate monitoring further improves robustness in operational settings.Effects of design factors, practical recommendations, methodological implications, and study limitations are discussed.