Cognitive Taxonomy of Items Based on Capability- Complexity Model Case Study: differential calculi test items

Developing complicate items without any specified extremities in calculation algorithms cause to inappropriate trends such as teaching to the test and test preparation strategies in examinees, and education inefficiencies to follow society demands has been hindering to achieve sustainable educational goals toward national development. Having exact educational standards and applying taxonomy models, however, will help to obtain fine evaluation of educational programs, contents and students achievement. In this manuscript, a small sample of differential calculi multiple choice items test has been studied in behavioral objectives framework. 12 items of a 30 items test battery which administered on a 3409 sample of students has been classified by capability complexity model. Outcomes of 3 items have been reported in this article. Results had shown all the test items measuring procedural knowledge at level three. 92% of items were abstract, and simultaneously recalling multiple calculation algorithms require for responding correctly to each item. Tasks with one calculation algorithm and lower complexity had better statistical parameters such as scalability and their item information functions yielded much more information about examinees capability.