One of the important components in an intelligent tutoring system is the student model. This model is used to predict what the student may do next as well as to serve as a repository of past student solutions. The student model is important in that it can help to direct the student to unknown material when enough concepts have been mastered and to material that needs to be reviewed when the student is unsure. Some student models have tried to predict student solution steps by restricting the interface to the point where the student cannot make an unknown move. Others do not concentrate on prediction, but instead concentrate on remedying errors in problem solutions. Since the problem of prediction is difficult, another tool, the neural network, should prove useful. Neural networks have the ability to generalize over a set of student answers. This ability gives the network the capacity to answer as the student would on problems that the network have never seen before. Given this exciting possibility, research has been started using the backpropagation model of neural networks to learn a student's method in performing subtraction. The preliminary results reported in this paper are encouraging and serve to show the promise of neural networks in the student model of intelligent tutoring systems.