Improving Instructional Decision-Making Using Diagnostic Classification Models

Abstract

For assessments to fulfill their purpose, the psychometric model must align with the intended uses of the assessment. In recent years, educators, administrators, and policymakers have called for assessments that require less time, but also provide more actionable results to aid stakeholders in making 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 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 and future directions for research.