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A strategy for controlling item exposure in multidimensional computerized adaptive testing

机译：在多维计算机化自适应测试中控制项目曝光的策略

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Although computerized adaptive tests have enjoyed tremendous growth, solutions for important problems remain unavailable. One problem is the control of item exposure rate. Because adaptive algorithms are designed to select optimal items, they choose items with high discriminating power. Thus, these items are selected more often than others, leading to both overexposure and underutilization of some parts of the item pool. Overused items are often compromised, creating a security problem that could threaten the validity of a test. Building on a previously proposed stratification scheme to control the exposure rate for one-dimensional tests, the authors extend their method to multidimensional tests. A strategy is proposed based on stratification in accordance with a functional of the vector of the discrimination parameter, which can be implemented with minimal computational overhead. Both theoretical and empirical validation studies are provided. Empirical results indicate significant improvement over the commonly used method of controlling exposure rate that requires only a reasonable sacrifice in efficiency.
机译：尽管计算机化自适应测试取得了长足的发展，但仍然没有针对重要问题的解决方案。一个问题是物品暴露率的控制。由于自适应算法旨在选择最佳项目，因此它们选择具有较高区分能力的项目。因此，与其他项目相比，这些项目的选择频率更高，从而导致项目库某些部分的暴露过度和利用不足。过度使用的项目通常会受到威胁，从而产生可能威胁到测试有效性的安全性问题。基于先前提出的分层方案来控制一维测试的曝光率，作者将其方法扩展到了多维测试。根据鉴别参数的向量的功能，提出了基于分层的策略，该策略可以以最小的计算开销来实现。提供了理论和经验验证研究。实验结果表明，与通常只需要合理牺牲效率的控制曝光率的方法相比，已有显着改进。

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