Modeling User Language Proficiency In A Writing Tutor For Deaf Learners Of English

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 proficiency of the user and is designed to be referenced during both writing analysis and feedback production. We motivate our model design by citing relevant research on second language and cognitive skill acquisition, and briefly discuss preliminary empirical evidence supporting the design. We conclude by showing how our design can provide a rich and robust information base to a language assessment / correction application by modeling user proficiency at a high level of granularity and specificity. 1 I n t r o d u c t i o n In order for any human language tutor to be effective, he or she must have an accurate picture of the student 's language acquisition status. This "picture" is used for selecting target features for tutoring and for shaping and tailoring the tutorial instruction. Automated tutoring systems emulate this desirable practice by constructing and maintaining a model of the user's knowledge, consulted at many levels of the tutorial production process. In this paper we will discuss the proposed knowledge modeling architecture of ICICLE (Interactive Computer Identification and Correction of Language Errors), a system under development (McCoy and Masterman (Michaud), 1997) whose goal is to provide deaf students with constructive tutoring on their written English. The target learner group for ICICLE is native or near-native users of American Sign Language (ASL). This population poses unique challenges for a writing instruction program: their writing contains many errors which are not made by native users of English, and students vary widely across levels of language ability, with some college-age writers producing near-nativelike English and others struggling with grammatical basics. Because of these characteristics of the learner population, it is integral to ICICLE's goal of user-tailored instruction that it account for user differences so that the instruction it provides is appropriate for a learner at any level. Since ASL is a distinct and vastly different language from English (Baker and Cokely, 1980), we view the acquisition of written English skills to be a task in second language acquisition for these learners (Michaud and McCoy, 1998). We are therefore proposing a user model design which incorporates a representation of the language acquisition process, and we have based our design upon current research in language acquisition and in the acquisition of cognitive skills. ICICLE will consult this model to obtain specific information about the user's current language knowledge, as well as about what knowledge is likely to be learnable by the user at the current time. In the following sections, we overview the ICICLE system architecture in order to explain how the user model will be utilized in system operation; we then discuss our design for modeling the second language acquisition process, and overview issues involved in implementing the model within our system. 2 I C I C L E O v e r v i e w ICICLE interacts with its user through a cycle of user input and system response. The user begins this cycle by supplying the system with a multi-sentential piece of writing for analysis. An error identification component uses an English grammar augmented with rules to