Effect of data/q matrix augmentation on latent class distributions in cognitive diagnostic models

In this study, the effect of partial scoring on implicit class invariance according to cognitive diagnostic models was investigated in multiple choice and structured responsive item formats, and it was aimed to make more accurate talent estimation on mixed test using data and Q matrix expansion methods. Furthermore, the usability of expanding and reconstructing the Q matrix in cognitive diagnostic models is examined. The measurement tool used in the thesis is the tests designed to make talent estimation by using BTM consisting of structured responses and multiple choice items. The tests were prepared for the research carried out within the scope of TUBITAK Project 115K531 and the data of the study were obtained from the tests of mathematics applied to 6th grade students under TUBITAK Project 115K531. The items were designed to measure high-level thinking skills. In order to prepare the items, first of all, substance writing training related to high level thinking skills was given. The items were then selected from the question pool by the referees, including field experts. After the pilot applications, the test was prepared and the main applications were made. The analysis revealed that the test had the same level and psychometric properties as PISA and TIMMS. In the research, feature sets that the test and items could be related to were determined and Q matrices were formed according to the cognitive diagnostic model. With the Q matrix determined by the field experts, the items were scored as 1-0 and talent estimation was performed. Then, the Q matrix was restructured and the items that could be scored partially were determined. Then, the items were re-scored and talent estimation was performed. In this way, the number of substances has doubled. Ability estimations were made at the classification level with the expanded data and the obtained data were compared with the results obtained from the first data. In addition, the effect of partial scoring of multiple choice and structured responding items on parameter estimations was examined. At the end of the study, it was observed that when Q matrix and data were expanded so as to be partially scored according to the structure of the item, it was possible to obtain more detailed information about the students, to see the students on a broader skill level and to give them a clearer feedback.