In this paper we discuss a proposed user knowledge modeling architecture for the ICICLE system, a language tutoring application for deaf learners of written English. The model will represent the language pro ciency of the user and is designed to be referen

cover grammatical errors commonly produced by our learner population to process the user's writing, tagging the grammatical errors it nds (Suri and McCoy, 1993 Schneider and McCoy, 1998). In the current implementation of ICICLE, the identi ed errors are highlighted in a window-based interface using colors which indicate the class of error for example, all subjectverb agreement errors are highlighted in blue. The user may then explore particular sentences containing errors by clicking on them, causing an editing window to appear with a simple onesentence\canned" response explaining the error. The user may then edit the sentence, have it reanalyzed by the system, and paste the results back into the original text. In the completed system, errors identi ed by the system will be passed to a response generation component which will construct one or more natural language explanations to discuss the nature of the error(s) with the student, who will then be prompted to make corrections and request a new analysis as in the current implementation. Both of the active processes in the ICICLE architecture (error identi cation and response generation) will access the user language acquisition model, which is currently under development. The error identi cation module will use the model to determine between multiple interpretations of a sentence which may correspond to di erent perceived errors in the text (McCoy et al., 1996). The essence of this determination is discerning the cause of the error. For instance, if the phrase\My brother like to go..."1 has occurred in the writing of a student, there are several possible situations that could have led to this mistake: the student could be entirely unaware of the English rule for subject/verb agreement the student could know of the rule, but has applied it incorrectly or the student has simply mistyped. To determine which of these possibilities is correct, it is necessary for the error analysis component to have at its disposal a model of the student's language knowledge which indicates his or her mastery of such language features as the concept of subject/verb agreement. (In the present system the choice between multiple interpretaThis example has been taken from our corpus of deaf writing samples.1

tions is not yet made on a principled basis.) We also wish for ICI

CLE to give instruction only on those language features which are at the user's current level of acquisition errors on features above this level are likely to be beyond the user's understanding, while errors on features which are well-established are likely to be simple mistakes which do not require instruction. The user model will therefore be consulted at the point where the error identi cation module passes the list of errors to the response generation module, trimming o those errors outside the current level. The motivation for this action is discussed further in Section 3. Lastly, the user model will be consulted during the planning of the system response. In order to structure explanations of a given language feature, the text planner needs to know the user's depth of related knowledge, including whether or not the user knows the concepts which are mentioned in the explanation. In the nal stages of response generation, the surface generator will also need to consult the model of acquisition in order to determine which grammatical constructs are known and thus understandable to the user, and which he or she may obtain the most bene t from viewing as positive examples. We have therefore established that a user model which contains a representation of second language pro ciency, speci c to the detail of individual language features and the user's mastery of them, is essential to the envisioned operation of the ICICLE system. We will now discuss our proposed design for this model and overview some of the issues we face in implementing this design.

3 Modeling Theories of Cognitive Skill and Second Language Acquisition

We see our model as representing the user's location along the path toward acquiring written English as a second language. To design this model, we have looked into the interlanguage theory of second language acquisition (Selinker, 1972 Ellis, 1994 Cook, 1993). In this theory, a learner generates in the L2 using a grammar which is his or her hypothesis of the target language. The learner systematically revises this internal representation as the language is ac-