Diagnosing Skills and Misconceptions with Bayesian Networks Applied to Diagnostic Multiple-Choice Tests

We discuss the use of Bayesian networks as a general framework for diagnostic classification in educational assessments, showing how they can accommodate sophisticated capabilities useful in diagnostic assessment, including modeling hierarchical structure among latent attributes, diagnostic use of information from incorrect alternatives in multiple-choice items, and simultaneous diagnosis of both subskills and misconceptions. These capabilities are illustrated with an application reported by (Lee 2003; Lee & Corter, 2003, 2011), who proposed using Bayesian networks as the inference engine to learn from test data and diagnose individuals’ misconceptions or bugs in the domain of multicolumn subtraction. Lee and Corter demonstrated that diagnosis of misconceptions or bugs is most effective when information from incorrect alternatives in multiple-choice items is used and when both bugs and skills are assessed simultaneously, with a hierarchical structure assumed for subskills and misconceptions. More recently, these innovations and issues have been investigated in the context of traditional CDM models. In this paper, we describe the approach taken by Lee and Corter and discuss some advantages and disadvantages of using Bayesian networks for diagnostic assessment.