In this article, it is shown how item text can be represented by (a) 113 features quantifying the text's linguistic characteristics, (b) 16 measures of the extent to which an information‐retrieval‐based automatic question‐answering system finds an item challenging, and (c) through dense word representations (word embeddings). Using a random forests algorithm, these data then are used to train a prediction model for item response times and predicted response times then are used to assemble test forms. Using empirical data from the United States Medical Licensing Examination, we show that timing demands are more consistent across these specially assembled forms than across forms comprising randomly‐selected items. Because an exam's timing conditions affect examinee performance, this result has implications for exam fairness whenever examinees are compared with each other or against a common standard.

中文翻译：

---

使用自然语言处理来预测项目响应时间并改善测试结构

本文展示了如何通过（a）量化文本语言特征的113个特征来表示项目文本，（b）基于信息检索的自动问答系统发现项目具有挑战性的程度的16种度量，以及（c）通过密集的词表示形式（词嵌入）。使用随机森林算法，然后将这些数据用于训练项目响应时间的预测模型，然后将预测的响应时间用于组装测试表格。使用来自美国医疗执照考试的经验数据，我们显示，在这些特殊组装的表格中，与在包含随机选择的项目的表格中相比，计时要求更加一致。由于考试的时间安排会影响应试者的表现，