Tracking Attribute Mastery Change among Individuals: Longitudinal Diagnostic Classification Models with Random Intercepts

Tracking an individual’s learning status is important for ensuring improvement. previously developed longitudinal diagnostic classification models (LDCMs) are limited in interpreting attribute mastery transitions as individual processes because they ignore group- and individual-level components. Therefore, considering such group- and individual-level variations as random intercepts (RI), the RI-LDCM was developed. A Bayesian estimation method for the RI-LDCM was also developed. A simulation study revealed that ignoring multilevel structures causes biased parameter estimates and serious under-coverages of posterior credible intervals. A real data example provided attribute mastery transitions from the four-year longitudinal data of mathematics tests in the third to sixth grades of Japanese elementary schools. The proposed RI-LDCM was also discussed.