Replenishing item pools for on-line ability testing requires innovative and efficient data collection. A method is proposed to collect test item calibration data in an on-line testing environment sequentially using locally D-optimum designs, thereby achieving high Fisher information for the item parameters. (SLD)

This paper argues that no single measurement strategy serves all purposes and that applying methods and techniques which allow a variety of data elements to be retrieved and juxtaposed may be an investment in the future. Item response theory, Rasch model, and latent trait theory are all approaches to a single conceptual topic. An abbreviated look…