Faculty of Psychology and Educational Sciences and ITEC-iMinds, University of Leuven-Kulak, Kortrijk, Belgium,
Behav Res Methods. 2014 Sep;46(3):823-40. doi: 10.3758/s13428-013-0413-3.
This article describes a generalized longitudinal mixture item response theory (IRT) model that allows for detecting latent group differences in item response data obtained from electronic learning (e-learning) environments or other learning environments that result in large numbers of items. The described model can be viewed as a combination of a longitudinal Rasch model, a mixture Rasch model, and a random-item IRT model, and it includes some features of the explanatory IRT modeling framework. The model assumes the possible presence of latent classes in item response patterns, due to initial person-level differences before learning takes place, to latent class-specific learning trajectories, or to a combination of both. Moreover, it allows for differential item functioning over the classes. A Bayesian model estimation procedure is described, and the results of a simulation study are presented that indicate that the parameters are recovered well, particularly for conditions with large item sample sizes. The model is also illustrated with an empirical sample data set from a Web-based e-learning environment.
本文描述了一种广义的纵向混合项目反应理论（IRT）模型，该模型允许在从电子学习（e-learning）环境或其他导致大量项目的学习环境中获得的项目反应数据中检测潜在的群体差异。所描述的模型可以看作是纵向 Rasch 模型、混合 Rasch 模型和随机项目 IRT 模型的组合，并且它包含了解释性 IRT 建模框架的一些特征。该模型假设项目反应模式中存在潜在类别，这是由于学习发生前的初始个体水平差异、潜在类别特定的学习轨迹或两者的组合所致。此外，它允许在类别上进行不同的项目功能。描述了一种贝叶斯模型估计过程，并呈现了模拟研究的结果，表明参数得到了很好的恢复，特别是在具有大项目样本量的条件下。该模型还通过来自基于 Web 的电子学习环境的实证样本数据集进行了说明。
