A statistical framework for dynamic cognitive diagnosis in digital learning environments

This study advances cognitive diagnostic assessments by proposing a novel statistical framework that integrates Cognitive Diagnosis Models with longitudinal data, including assessments of multiple skill sets. Our paper enhances the accuracy of attribute mastery evaluations and the assessment of covariate impacts on learning transitions. The applicability of the method is demonstrated through real-world application.

Recommended citation: Ma, Y., Cain, K., Ushakova, A., & Wallin, G. (2024). A statistical framework for dynamic cognitive diagnosis in digital learning environments. Manuscript under peer review. Preregistration: https://osf.io/nqkub
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