A Systematic Review of the Implications of Diagnostic Classification Modeling for Cognitive Models of Mathematics

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In education, cognitive models are often representations of knowledge and processes required to solve a task. These models can be used as foundations of pedagogy, curricula, and educational software. Diagnostic classification modeling (DCM) is a family of statistical approaches designed to combine responses to an assessment with a cognitive model to estimate students’ mastery of each skill in the assessment. This article systematically reviews the use of DCM with mathematics tests, focusing on the implications for developing and improving cognitive models. The literature was systematically searched, finding 182 articles analyzing mathematics assessments with DCM. This review highlights insights from DCM studies across a range of topics, including the sequences in which skills are learned, differences between problem-solving strategies, differences in learning across education systems, and the role of foundational knowledge when learning more advanced skills. Concerns about some uses of DCM are raised and future research directions are recommended.