Cognitive Diagnostic Modeling Analysis of the Reading Comprehension Section of an Iranian High-stakes Language Proficiency Test

The purpose of this study was to compare the functioning of five restrictive CDMs, including DINA, DINO, A-CDM, LLM, and RRUM, against the G-DINA model to identify the best-fitting CDM which can better explain the interaction underlying the attributes of the reading comprehension section of an Iranian high-stakes language proficiency test. To achieve this aim, item responses of ۱۱۵۲ examinees to the items of the test were examined. The six CDMs were initially compared in terms of relative and absolute fit statistics at test-level to choose the best model. It was found that the G-DINA model outperformed compared to the restrictive models; thus, it was selected for the second phase of the study. Concerning the second purpose of the study, the G-DINA was used to identify strengths and weaknesses of the examinees. The results revealed that making an inference and vocabulary are the hardest attributes for examinees of the test, and understanding the specific information is the easiest attribute. Finally, the models were also compared at item-level. The presence of a combination of L۲ reading attributes was found.

Keywords:

Reading comprehension attributes , CDMs , G-DINA , compensatory , non-compensatory