Previous designs for online calibration have only considered examinees’ responses to items. However, the use of response time, a useful metric that can easily be collected by a computer, has not yet been embedded in calibration designs. In this article we utilize response time to optimize the assignment of new items online, and accordingly propose two new adaptive designs. These are the D-optimal per expectation time unit design (D-ET) and the D-optimal per time unit design (D-T). The former method uses the conditional maximum likelihood estimation (CMLE) method to estimate the expected response times, while the latter employs the nonparametric k-nearest-neighbour method to predict the response times. Simulations were conducted to compare the two new designs with the D-optimal online calibration design (D design) in the context of continuous online calibration. In addition, a preliminary study was carried out to evaluate the performance of CMLE prior to its application in D-ET. The results showed that, compared to the D design, the D-ET and D-T designs saved response time and accrued larger calibration information per time unit, without sacrificing item calibration precision.

中文翻译：

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用于连续在线项目校准的单位时间最大信息量设计

以前的在线校准设计只考虑了考生对项目的反应。然而，响应时间的使用，一个可以很容易地被计算机收集的有用指标，尚未嵌入校准设计中。在本文中，我们利用响应时间来优化在线新项目的分配，并相应地提出两种新的自适应设计。这些是 D 最优每期望时间单位设计 (D-ET) 和 D 最优每时间单位设计 (DT)。前一种方法使用条件最大似然估计 (CMLE) 方法来估计预期响应时间，而后者使用非参数k-最近邻方法来预测响应时间。在连续在线校准的背景下，进行了模拟以将两种新设计与 D 最优在线校准设计（D 设计）进行比较。此外，在将 CMLE 应用于 D-ET 之前，还进行了初步研究以评估其性能。结果表明，与 D 设计相比，D-ET 和 DT 设计在不牺牲项目校准精度的情况下，节省了响应时间，单位时间积累了更多的校准信息。