Title: Exploring Skill Condensation Rules for Cognitive Diagnostic Models in a Bayesian Framework
Description: Diagnostic paradigms are becoming an alternative to normative approaches in educational assessment. One of the principal objectives of diagnostic assessment is to determine skill proficiency for tasks that demand the use of specific cognitive processes. Ideally, diagnostic assessments should include accurate information about the skills required to correctly answer each item in a test, as well as any additional evidence about the interaction between those cognitive constructs. Nevertheless, little research in the field has focused on the types of interactions (i.e., the condensation rules) among skills in models for cognitive diagnosis. The present study introduces a Bayesian approach to determine the underlying interaction among the skills measured by a given item when comparing among models with conjunctive, disjunctive, and compensatory condensation rules. Following the reparameterization framework proposed by DeCarlo (2011), the present study includes transformations for disjunctive and compensatory models.

Next, a methodology that compares between pairs of models with different condensation rules is presented; parameters in the model and their distribution were defined considering former Bayesian approaches proposed in the literature. Simulation studies and empirical studies were performed to test the capacity of the model to correctly identify the underlying condensation rule. Overall, results from the simulation study showed that the correct condensation rule is correctly identified across conditions. The results showed that the correct condensation rule identification depends on the item parameter values used to generate the data and the use of informative prior distributions for the model parameters. Latent class sizes parameters for the skills and their respective hyperparameters also showed a good recovery in the simulation study. The recovery of the item parameters presented limitations, so some guidelines to improve their estimation are presented in the results and discussion sections.

The empirical studies highlighted the usefulness of this approach in determining the interaction among skills using real items from a mathematics test and a language test. Despite the differences in their area of knowledge and Q-matrix structure, results indicated that both tests are composed in a higher proportion of conjunctive items that demand the mastery of all skills.