The impact of computerized adaptive test termination rules on accuracy across different ability estimation methods

The study aimed to compare the termination rules of computerized adaptive testing (CAT), specifically the rule of termination after a fixed number of items versus the rule of termination based on the minimum standard error (SE), using the methods of maximum likelihood estimation (MLE) and maximum a posteriori. The goal was to assess the relative accuracy of each rule to determine which method provides the highest measurement accuracy. In order to address the objectives of the study, the researcher developed a mathematics item bank for the 6<sup>th</sup> and 7<sup>th</sup> grades consisting of 275 items. This bank was used to develop 6 achievement tests, three for 6th grade and three for 7<sup>th</sup> grade, with each test comprising 46 items. In addition, 10 items were common to all the tests and were used as a common core. The tests were conducted with 2612 students of class six and seven. For data and information analysis and processing, BILOG-MG-3.0, SPSS, and Fast Test Web v3.80.26 applications were used. Four different applications were performed on another sample of 403 students in order to evaluate the precision of the CAT procedure through several terminating criteria–a fixed criterion of 25 administered items and a criterion based on a SE of no larger than 0.25. From the results obtained, the accuracy of examinations is independent of the method used in estimating the parameters and that the determination of the fixed period is superior to the determination relating to SE. Moreover, the results revealed that adopting Bayes’ theorem and a termination rule determined by standard deviation improves precision of the estimation, though in a case where 25 items are used, MLE is the best.