Test scoring models vary in their generality, some even adjust for examinees answering multiple-choice items correctly by accident (guessing), but no models, that we are aware of, automatically adjust an examinee’s score when there is internal evidence of cheating. In this study, we use a combination of jackknife technology with an adaptive robust estimator to reduce the bias in examinee scores due to contamination through events such as having access to some of the test items in advance of the test administration. We illustrate our methodology with a data set of test items we knew to have been divulged to a subset of the examinees.

中文翻译：

---

用污染的响应向量计分测试

考试评分模型的通用性各不相同，有些甚至可以为考生偶然地正确回答多项选择（猜测）而做出调整，但是我们知道，没有模型可以在内部有作弊证据时自动调整考生的分数。在这项研究中，我们将折刀技术与自适应鲁棒估计器结合使用，以减少由于诸如在测试管理之前访问某些测试项目等事件而导致的污染而导致的考生分数偏差。我们用已知的测试项目数据集说明了我们的方法，这些测试项目已经泄露给一部分应试者。