The use of computerized adaptive testing algorithms for ranking items (e.g., college preferences, career choices) involves two major challenges: unacceptably high computation times (selecting from a large item pool with many dimensions) and biased results (enhanced preferences or intensified examinee responses because of repeated statements across items). To address these issues, we introduce subpool partition strategies for item selection and within‐person statement exposure control procedures. Simulations showed that the multinomial method reduces computation time while maintaining measurement precision. Both the freeze and revised Sympson‐Hetter online (RSHO) methods controlled the statement exposure rate; RSHO sacrificed some measurement precision but increased pool use. Furthermore, preventing a statement's repetition on consecutive items neither hindered the effectiveness of the freeze or RSHO method nor reduced measurement precision.

中文翻译：

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具有多维等级项目的计算机自适应测试的项目选择和曝光控制方法

使用计算机化的自适应测试算法对项目进行排名（例如，大学的偏好，职业选择）涉及两个主要挑战：难以接受的高计算时间（从具有多个维度的大型项目库中选择）和偏倚的结果（偏好增强或应试者反应加剧，因为跨项目的重复语句）。为了解决这些问题，我们介绍了子池分区策略，用于项目选择和人员内部语句暴露控制程序。仿真表明，多项式方法在保持测量精度的同时减少了计算时间。冻结和修订的Sympson-Hetter在线（RSHO）方法都控制了语句的暴露率；RSHO牺牲了一些测量精度，但增加了池使用量。此外，防止陈述