The purpose of this study is to find a formula that describes the relationship between item exposure parameters and item parameters in computerized adaptive tests by using genetic programming (GP) - a biologically inspired artificial intelligence technique. Based on the formula, item exposure parameters for new parallel item pools can be predicted without conducting additional iterative simulations. Results show that an interesting formula between item exposure parameters and item parameters in a pool can be found by using GP. The item exposure parameters predicted based on the found formula were close to those observed from the Sympson and Hetter (1985) procedure and performed well in controlling item exposure rates. Similar results were observed for the Stocking and Lewis (1998) multinomial model for item selection and the Sympson and Hetter procedure with content balancing. The proposed GP approach has provided a knowledge-based solution for finding item exposure parameters.

中文翻译：

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在计算机化自适应测试中预测物品暴露参数。

这项研究的目的是找到一个公式，该公式通过使用遗传编程（GP）（一种生物学启发的人工智能技术）来描述计算机自适应测试中的物品暴露参数和物品参数之间的关系。基于该公式，无需进行额外的迭代模拟，就可以预测新的平行物料池的物料暴露参数。结果表明，使用GP可以找到物料暴露参数和池中物料参数之间有趣的公式。根据发现的公式预测的物品暴露参数与从Sympson和Hetter（1985）程序观察到的参数接近，并且在控制物品暴露率方面表现良好。Stocking和Lewis（1998）多项式模型用于项目选择以及具有内容平衡的Sympson和Hetter程序也观察到了相似的结果。提出的GP方法为查找项目暴露参数提供了一种基于知识的解决方案。