Extending focusing frameworks to process complex sentences and to correct the written English of proficient signers of American sign language
Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
An intelligent tutoring system for deaf learners of written English
Assets '00 Proceedings of the fourth international ACM conference on Assistive technologies
Supporting Intelligent Tutoring in CALL by Modeling the User's Grammar
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference
Modeling user interlanguage in a second language tutoring system for deaf users of american sign language
The reliability of a dialogue structure coding scheme
Computational Linguistics
Recognizing syntactic errors in the writing of second language learners
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Error profiling: toward a model of English acquisition for deaf learners
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Evaluating a model to disambiguate natural language parses on the basis of user language proficiency
UM'03 Proceedings of the 9th international conference on User modeling
User Modeling and User-Adapted Interaction
Capturing the Evolution of Grammatical Knowledge in a CALL System for Deaf Learners of English
International Journal of Artificial Intelligence in Education
Personalized Teaching of a Programming language over the web: Stereotypes and rule-based mechanisms
Proceedings of the 2008 conference on Knowledge-Based Software Engineering: Proceedings of the Eighth Joint Conference on Knowledge-Based Software Engineering
Evaluating a model to disambiguate natural language parses on the basis of user language proficiency
UM'03 Proceedings of the 9th international conference on User modeling
Review: Student modeling approaches: A literature review for the last decade
Expert Systems with Applications: An International Journal
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The work described here pertains to ICICLE, an intelligent tutoring system for which we have designed a user model to supply data for intelligent natural language parse disambiguation. This model attempts to capture the user's mastery of various grammatical units and thus can be used to predict the grammar rules he or she is most likely using when producing language. Because ICICLE's user modeling component must infer the user's language mastery on the basis of limited writing samples, it makes use of an inferencing mechanism that will require knowledge of stereotypic acquisition sequences in the user population. We discuss in this paper the methodology of how we have applied an empirical investigation into user performance in order to derive the sequence of stereotypes that forms the basis of our modeling component's reasoning capabilities.