Computational models of learning can be powerful tools to test educational technologies, automate the authoring of instructional software, and advance theories of learning. These mechanistic models of learning, which instantiate computational theories of the learning process, are capable of making predictions about learners' performance in instructional technologies given only the technology itself without fitting any parameters to existing learners' data. While these so call "zero-parameter" models have been successful in modeling student learning in intelligent tutoring systems they still show systematic deviation from human learning performance. One deviation stems from the computational models' lack of prior knowledge--all models start off as a blank slate--leading to substantial differences in performance at the first practice opportunity. In this paper, we explore three different strategies for accounting for prior knowledge within computational models of learning and the effect of these strategies on the predictive accuracy of these models. [For the full proceedings, see ED599096.]