Qasim Khlaif Kadhim

Diagnostic classification models (DCMs) have recently become very popular both for research purposes and for real testing endeavors for student assessment. A plethora of DCM models give researchers and practitioners a wide range of options for student diagnosis and classification. One intriguing option that some DCM models offer is the possibility of examining the nature of the interactions among the attributes underlying a skill. Attributes in second/foreign language (L2) may interact with each other in a compensatory/non-compensatory manner. Subskill/attribute relationship has been studied using diagnostic classification models. The present study provides a mini review of the DCM studies on the attribute relationships in L2 reading, listening, and writing. The criteria based on which interaction between the attributes have been inferred are reviewed. The results showed that the majority of DCM studies have investigated reading comprehension and more studies are required on the productive skills of writing and speaking. Furthermore, suggestions for future studies are provided.

A C-Test is a gap-filling test for measuring language competence in the first and second language. C-Tests are usually analyzed with polytomous Rasch models by considering each passage as a super-item or testlet. This strategy helps overcome the local dependence inherent in C-Test gaps. However, there is little research on the best polytomous Rasch model for C-Tests. In this study, the Rating Scale Model (RSM) and the Partial Credit Model (PCM) for analyzing C-Tests were compared. To this end, a C-Test composed of six passages with both RSM and PCM was analyzed. The models were compared in terms of overall fit, individual item fit, dimensionality, test targeting, and reliability. Findings showed that, although the PCM has a better overall fit compared to the RSM, both models produce similar test statistics. In light of the findings of the study, the choice of the best Rasch model for C-Tests will be discussed.