A Stochastic Method for Balancing Item Exposure Rates in Computerized Classification Tests

摘要

Computerized classification tests (CCTs) classify examinees into categories such as pass/fail, masteronmaster, and so on. This article proposes the use of stochastic methods from sequential analysis to address item overexposure, a practical concern in operational CCTs. Item over-exposure is traditionally dealt with in CCTs by the Sympson-Hetter (SH) method, but this method is unable to restrict the exposure of the most informative items to the desired level. The authors' new method of stochastic item exposure balance (SIEB) works in conjunction with the SH method and is shown to greatly reduce the number of overexposed items in a pool and improve overall exposure balance while maintaining classification accuracy comparable with using the SH method alone. The method is demonstrated using a simulation study.