Paper detail

Meeting an absolute test information target with optimal number of test items via Grand Canonical Monte Carlo simulation

This work studies IRT-based Automated Test Assembly (ATA) of multiple test forms (tests) that meet an absolute target information function, i.e. selecting from an item bank only the tests that have information functions that are at a small distance away from the target. The authors introduce the quantities multiplicity of tests and probability of selecting a test with particular number of items N and distance E from the target. A Grand Canonical Monte Carlo test-assembly algorithm is proposed that selects tests according to this probability. The algorithm allows N to vary during the simulation. This work demonstrates that the number of tests that meet the target depends strongly on N. The algorithm is capable of finding tests with small values of E and various values of N depending on the need of the test constructor. Most importantly, it can determine the optimal N for which a maximal number of tests with certain specified small E exists.