Transfer models provide a viable means of determining which skills a student needs in order to solve a given problem. However, constructing a good fitting transfer model requires a lot of trial and error. The main goal of this thesis was to develop a procedure for developing better fit transfer models for intelligent tutoring systems. The procedure implements a search method using association rules as a means of guiding the search. The association rules are mined from the instances in the dataset that the transfer model predicts incorrectly. The association rules found in the mining process determines what operation to perform on the current transfer model. Our search algorithm using association rules was compared to a blind search method that finds all possible transfer models for a given set of factors. Our search process was able to find statistically similar models to the ones the blind search method finds in a considerably shorter amount of time. The difference in times between our search process and the blind search method is days to minutes. Being able to find good transfer models quicker will help intelligent tutor system builders as well as cognitive science researchers better assess what makes certain problems hard and other problems easy for students.