<p>The development of intelligent tutoring systems and other educational technology necessitated the implementation and development of computational models of student learning. At the foundation of these models is the assumption that they accurately implement and track human cognitive processes. However, the extent to which this assumption is correct requires testing against empirical data. In the current paper, we use data from a large-scale longitudinal lab study to investigate the match between the processes instantiated in the models and human memory and learning processes. When fit to all sessions retrospectively, the selected models of student learning (Bayesian Knowledge Tracing, Bayesian Knowledge Tracing + Forgetting, and Additive Factors Model) capture the qualitative trends of learning across sessions and relatively acceptable fit metrics. However, when the models are used to predict future behavior (time-based cross-validation), as is often the goal in applied contexts, the picture changes considerably. We show that these popular types of student learning models fail to account for basic cognitive principles—the spacing effect, and patterns of forgetting and learning across sessions. In fact, in some instances, having a poor model of human learning and memory may perform as well as having no model of human learning and memory at all.</p>