With the widespread use of computers in modern assessment, online calibration has become increasingly popular as a way of replenishing an item pool. The present study discusses online calibration strategies for a joint model of responses and response times. The study proposes likelihood inference methods for item paramter estimation and evaluates their performance along with optimal sampling procedures. An extensive simulation study indicates that the proposed online calibration strategies perform well with relatively small samples (e.g., 500∼800 examinees). The analysis of estimated parameters suggests that response time information can be used to improve the recovery of the response model parameters. Among a number of sampling methods investigated, A-optimal sampling was found most advantageous when the item parameters were weakly correlated. When the parameters were strongly correlated, D-optimal sampling tended to achieve the most accurate parameter recovery. The study provides guidelines for deciding sampling design under a specific goal of online calibration given the characteristics of field-testing items.

中文翻译：

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计算机自适应测试中项目响应和响应时间联合模型的在线校准

随着计算机在现代评估中的广泛使用，在线校准已成为一种补充项目库的方式越来越受欢迎。本研究讨论了响应和响应时间的联合模型的在线校准策略。该研究提出了用于项目参数估计的似然推断方法，并评估了它们的性能以及最佳抽样程序。大量的仿真研究表明，所提出的在线校准策略在相对较小的样本（例如500至800名考生）中表现良好。估计参数的分析表明，响应时间信息可用于改善响应模型参数的恢复。在研究的许多抽样方法中，当项目参数之间的相关性较弱时，发现A最优抽样最为有利。当参数高度相关时，D最优采样趋于实现最准确的参数恢复。根据现场测试项目的特点，该研究为根据在线校准的特定目标决定采样设计提供了指导。