The distinction between quantitative and qualitative differences in mastery is essential when monitoring student progress and is crucial for instructional interventions to deal with learning difficulties. Mixture item response theory (IRT) models can provide a convenient way to make the distinction between quantitative and qualitative differences…

This study introduces an approach for modeling multidimensional response data with construct-relevant group and domain factors. The item level parameter estimation process is extended to incorporate the refined effects of test dimension and group factors. Differences in item performances over groups are evaluated, distinguishing two levels of…