Two New Models for Item Preknowledge

Abstract

To evaluate preknowledge detection methods, researchers often conduct simulation studies in which they use models to generate the data. In this article, we propose two new models to represent item preknowledge. Contrary to existing models, we allow the impact of preknowledge to vary across persons and items in order to better represent situations that are encountered in practice. We use three real data sets to evaluate the fit of the new models with respect to two types of preknowledge: items only, and items and the correct answer key. Results show that the two new models provide the best fit compared to several other existing preknowledge models. Furthermore, model parameter estimates were found to vary substantially depending on the type of preknowledge being considered, indicating that answer key disclosure has a profound impact on testing behavior.