2019/09/30 by Wim J. van der Linden, Seung W. Choi
paper · doi:10.1111/jedm.12254
crossref issued 2019/09/30 · crossref published 2019/09/30 · crossref published-online 2019/09/30 · crossref created 2019/10/01 · crossref published-print 2020/09/01 · crossref deposited 2023/09/05 · crossref indexed 2026/07/28
Abstract One of the methods of controlling test security in adaptive testing is imposing random item‐ineligibility constraints on the selection of the items with probabilities automatically updated to maintain a predetermined upper bound on the exposure rates. Three major improvements of the method are presented. First, a few modifications to improve the initialization of the method and accelerate the impact of its feedback mechanism on the observed item‐exposure rates are introduced. Second, the case of conditional item‐exposure control given the uncertainty of examinee's ability parameter is addressed. Third, although rare for a well‐designed item pool, when applied in combination with the shadow‐test approach to adaptive testing the method may meet occasional infeasibility of the shadow‐test model. A big M method is proposed that resolves the issue. The practical advantages of the improvements are illustrated using simulated adaptive testing from a real‐world item pool under a variety of conditions.