When modeling student learning, tutors that use the Knowledge Tracing framework often assume that all students have the same set of model parameters. We find that when fitting parameters to individual students, there is significant variation among the individual's parameters. We examine if this variation is important in terms of instructional decisions by computing the difference in the expected number of practice opportunities required if mastery is assessed using an individual student's own estimated model parameters, compared to the population model. In the dataset considered, we find that a significant portion of students are expected to perform twice as many practice opportunities if the student is modeled using a population-based model, compared to the number needed if the student's own model parameters were used. We also find an additional significant portion of students will be likely to receive less practice opportunities than needed, implying that such students will be advanced too early. Though further work on additional datasets is needed to explore this issue in more depth, our results suggest that considering individual variation in student parameters may have important implications for the instructional decisions made in intelligent tutoring systems that use a Knowledge Tracing model. (Contains 3 figures and 1 footnote.) [This paper was supported by Google. For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]