Reading comprehension attributes

Cognitive diagnostic models (CDMs) have received sustained attention in educational settings because they can be used to operationalize formative assessment to provide diagnostic feedback and inform instruction. A large number of CDMs have been developed over the past few years. An important component of all CDMs is a Q-matrix that specifies a particular hypothesis about the relationship between each test item and its required attributes. The purpose of this study was to construct and validate a Q-matrix for the reading comprehension section of the Islamic Azad University English Proficiency Test (IAUEPT), as an advanced English placement test designed to measure language ability of Ph.D. candidates who tend to pursue their studies in the IAU. To achieve this, using item responses of 1152 candidates to twenty items of the reading section of the test, an initial Q-matrix was constructed based on theories and models of second/foreign language (L2) reading comprehension, previous applications of CDMs on L2 reading comprehension, and brainstorming and consensus of five content experts. Then, the initial Q-matrix was empirically validated using the method proposed by de la Torre and Chiu (2016) and checking mesa plots, and heatmap plot. Five attributes were derived for the reading comprehension section: vocabulary, grammar, making an inference, understanding specific information, and identifying explicit information. Finally, the analysis of the Generalized Deterministic Inputs, Noisy “and” Gate (GDINA) regarding absolute fit at item- and test-level as well as three residual-based statistics showed the accuracy of the Q-matrix and a perfect model-data fit.

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 1152 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 L2 reading attributes was found.