Modeling student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for modeling the acquisition of knowledge is Bayesian Knowledge Tracing (BKT). Various extensions to the original BKT model have been proposed, among them two novel models that unify BKT and Item Response Theory (IRT). Latent Factor Knowledge Tracing (LFKT) and Feature Aware Student knowledge Tracing (FAST) exhibit state of the art prediction accuracy. However, only few studies have analyzed the characteristics of these different models. In this paper, we therefore evaluate and compare properties of the models using synthetic data sets. We sample from a combined student model that encompasses all four models. Based on the true parameters of the data generating process, we assess model performance characteristics for over 66'000 parameter configurations and identify best and worst case performance. Using regression we analyze the influence of different sampling parameters on the performance of the models and study their robustness under different model assumption violations. [For complete proceedings, see ED560503.]