Improving Instructional Decision-Making Using Diagnostic Classification Models

Article Abstract 

In recent years, educators, administrators, policymakers, and measurement experts have called for assessments that support educators in making better instructional decisions. One promising approach to measurement to support instructional decision-making is diagnostic classification models (DCMs). DCMs are flexible psychometric models that facilitate fine-grained reporting on skills that students have mastered. In this paper, we describe how DCMs can be leveraged to support better decision making. We first provide a high-level overview of DCMs. We then describe different methods for reporting results from DCM-based assessments that support decision-making for different stakeholder groups. We close with a discussion of considerations for implementing DCMs in an operational setting, including how they can inform decision making at state and local levels, and share future directions for research.

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