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The study presents an exploratory graphical modeling approach for evaluatinglocal item dependency within cognitively diagnostic classification models(DCMs). Current approaches to modeling local dependence require known itemstructure and have limited utility when such information is not available. Inthis study, we propose an exploratory approach to modeling local dependence sothat items' own interactions can be revealed without dependency specification.The new framework is developed by integrating a Markov network into ageneralized DCM. The framework unveils item interactions while performingregular cognitive diagnosis within a unified scheme. The inference on the modelparameters is made on the regularized pseudo-likelihood and is implemented byan EM algorithm. Numerical experimentation from Monte Carlo simulation suggeststhat the proposed framework adequately recovers generating parameters andyields reliable standard error estimates. Compared with the regular DCM, thenew model produced more accurate item parameter estimates as items exhibitlocal dependence. The study demonstrates application of the model using tworeal assessment data and discusses practical benefits of modeling localdependence.

Markov Network for Modeling Local Item Dependence in Cognitively Diagnostic Classification Models