In data collected from virtual learning environments (VLEs), item response theory (IRT) models can be used to guide the ongoing measurement of student ability. However, such applications of IRT rely on unbiased item parameter estimates associated with test items in the VLE. Without formal piloting of the items, one can expect a large amount of nonignorable missing data in the VLE log file data, and this is expected to negatively affect IRT item parameter estimation accuracy, which then negatively affects any future ability estimates utilized in the VLE. In the psychometric literature, methods for handling missing data have been studied mostly around conditions in which the data and the amount of missing data are not as large as those that come from VLEs. In this article, we introduce a semisupervised learning method to deal with a large proportion of missingness contained in VLE data from which one needs to obtain unbiased item parameter estimates. First, we explored the factors relating to the missing data. Then we implemented a semisupervised learning method under the two-parameter logistic IRT model to estimate the latent abilities of students. Last, we applied two adjustment methods designed to reduce bias in item parameter estimates. The proposed framework showed its potential for obtaining unbiased item parameter estimates that can then be fixed in the VLE in order to obtain ongoing ability estimates for operational purposes.

在从虚拟学习环境 (VLE) 收集的数据中，项目反应理论 (IRT) 模型可用于指导对学生能力的持续测量。然而，IRT 的此类应用依赖于与 VLE 中的测试项目相关的无偏项目参数估计。如果不对项目进行正式试验，VLE 日志文件数据中可能会出现大量不可忽略的缺失数据，预计这会对 IRT 项目参数估计精度产生负面影响，进而对 VLE 中使用的任何未来能力估计产生负面影响。在心理测量文献中，处理缺失数据的方法主要围绕数据和缺失数据量不如来自 VLE 的数据和缺失数据量大的情况进行研究。在本文中，我们引入了一种半监督学习方法来处理 VLE 数据中包含的大量缺失，需要从中获得无偏项参数估计。首先，我们探讨了与缺失数据相关的因素。然后我们在二参数逻辑 IRT 模型下实现了一种半监督学习方法来估计学生的潜在能力。最后，我们应用了两种旨在减少项目参数估计偏差的调整方法。拟议的框架显示了其获得无偏项目参数估计的潜力，然后可以在 VLE 中修复这些参数估计，以便获得用于操作目的的持续能力估计。然后我们在二参数逻辑 IRT 模型下实现了一种半监督学习方法来估计学生的潜在能力。最后，我们应用了两种旨在减少项目参数估计偏差的调整方法。拟议的框架显示了其获得无偏项目参数估计的潜力，然后可以在 VLE 中修复这些参数估计，以便获得用于操作目的的持续能力估计。然后我们在二参数逻辑 IRT 模型下实现了一种半监督学习方法来估计学生的潜在能力。最后，我们应用了两种旨在减少项目参数估计偏差的调整方法。拟议的框架显示了其获得无偏项目参数估计的潜力，然后可以在 VLE 中修复这些参数估计，以便获得用于操作目的的持续能力估计。