We discuss a user model which can be tailored to different types of users in order to identify and correct English language errors. It is presented in the context of a written English tutoring system for deaf people who use American Sign Language.
Our approach to identifying the errors is to augment a standard grammar of English with a set of error productions (mal-rules) which expand the grammar accepted by the parser. We use a scoring mechanism to activate and initialize a subset of possible mal-rules for a given user. The scoring mechanism is affected by two major user modeling components. The first component is a static model of the first language which can be used to identify places where language transfer might take place. The second component is more dynamic and is based on a model of second language acquisition, zeroing in on aspects that may be causing current difficulties.
We motivate and explain the components of our model and then show how it can be generalized to other language assistance applications.