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

tiation of a knowledge base of morphosyntactic features, tagged to indicate each feature's placement within a given user's knowledge -\acquired" or\ZPD," depending on the user's performance on each feature. In the next section, we address how we expect to assign these tags in the initial state of the model. After initiation, it would be our expectation that over time those features indicated as being part of the ZPD would be tagged as\acquired" once they are used with consistent correctness, and features that had no tags previously (because they were absent in the learner's language production) would move into the ZPD once the learner is ready to begin acquiring them. The\feature" units in this knowledge base will be those features represented by the augmented grammar which ICICLE uses to parse its input, since that is the granularity of its error analysis capacity and of the feedback the system presents. This design answers the needs of both active modules of the ICICLE architecture. The error identi cation phase could use it when selecting a parse for a given portion of text. Because of the relationship between the granularity of the model and the grammar, the action of indicating the ZPD in the model could be mirrored in the grammar, with special notation given to those grammatical rules covering ZPD concepts. The parser can assume that structures tagged as\acquired" in the model representing this user will be used correctly with consistency, while those within the ZPD are most likely to occur with error, and those which are beyond the user's knowledge will be absent from his or her writing. When choosing a parse, the system should favor one using\correct" English grammar rules from the\acquired" range, and\incorrect" rules from the ZPD range. Thus the correct parse and source of error can be determined by comparing the possibilities against what constructions the user is expected to use correctly or incorrectly according to the model. A model of this type would also provide vital information needed for transforming a list of errors into the tutorial response. Instruction and corrective feedback on aspects of the knowledge within the ZPD may be bene cial, while instruction dealing with that outside of the Zon

e is likely to be ine ective or even detrimental. Tutoring on material outside the ZPD which has

SLALOMComplex+s verb+ed past+s poss+s plural+ing prog Simple A obj-rel sub-rel

det N adj N N prop-N B Feature Hierarchy

SVOO SVO

no rel SV S or V C D

Figure 1: SLALOM: Steps of Language Acquisition in a Layered Organization Model. already been mastered by the student is likely to bore them tutoring on material beyond the grasp of the student at this time is likely to produce confusion or frustration. When passing the error list to the response module, the error identi cation module can use the user's placement in the model to prune the errors so that the tutorial responses are focused only on those errors at the user's current level of language acquisition. The actual construction of the system response can also reference this model, using it to determine the user's depth of knowledge on the features being discussed so that appropriate background information and de nitions of terms being used can be provided. The full interaction between the text planner and the user model is a topic of current exploration. In formalizing our user model design, we therefore need to capture three aspects of language competence: the past, the present, and the future. The model must be able to indicate which features of language the user has already mastered, those features he is presently attempting to acquire, and those features that are above his current level. The next section discusses how we propose to structure this information in the model, and overviews our approach for building and maintaining it.

4 SLALOM: A Proposed Model Architecture

Our proposed architecture for capturing the theories expressed in the previous section is SLALOM (Steps of Language Acquisition in a Layered Organization Model). A very simpli ed representation of SLALOM can be found in Fig-

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

ure 1. SLALOM captures the stereotypic linear order of acquisition within certain categories of morphological and/or syntactic features of language, such as NP or relative clause formation. Within a category, depicted as a stack of features in the gure, a given morphosyntactic feature is expected to be acquired subsequent to those below it, and prior to those above it. Lateral connections between the categories indicate features which we expect to be acquired concurrently. As mentioned in the previous section, an instantiation of this model would represent a given user by tagging the features as acquired or within the ZPD according to observations of the user's language performance on texts analyzed by the system. Once such observations have been noted, inferring additional information about non-tagged elements would be possible through exploiting the lateral connections to infer a concurrent relationship or exploiting the orders within hierarchies to infer whether a feature is likely to have been acquired by this user. The explicitly-marked tags may be revised over time as the learner's pro c

iency develops, with those features tagged as within the ZPD moving to acquired status, and new features from the not-ye …… 此处隐藏：6770字，全部文档内容请下载后查看。喜欢就下载吧 ……