In this paper, we present a methodology for the development of a user model for CALL which captures various levels of language acquisition using individualized overlays supported with stereotypes. Our current focus is the empirical analysis of the order of written English grammatical structure acquisition in our learner population used to develop stereotype layers in our model.

This paper proposes a method to correct English verb form errors made by non-native speakers. A basic approach is template matching on parse trees. The proposed method improves on this approach in two ways. To improve recall, irregularities in parse trees caused by verb form errors are taken into account; to improve precision, n-gram counts are utilized to filter proposed corrections. Evaluation on non-native corpora, representing two genres and mother tongues, shows promising results. 1

Student models for Intelligent Computer Assisted Language Learning (ICALL) have largely focused on the acquisition of grammatical structures. In this paper, we motivate a broader perspective of student models for ICALL that incorporates insights from current research on second language acquisition and language testing. We argue for a student model that includes a representation of the learner’s ability to use language in context and to perform tasks as well as for an explicit activity model that provides information on the language tasks and the inferences for the student model they support. The student model architecture we present is being developed as part of the TAGARELA system, an intelligent workbook supporting the instruction of Portuguese. 1

While a wide variety of grammatical mistakes may be observed in the speech of non-native speakers, the types and frequencies of these mistakes are not random. Certain parts of speech, for example, have been shown to be especially problematic for Japanese learners of English [1]. Modelling these errors can potentially enhance the performance of computer-assisted language learning systems. This paper presents an automatic method to estimate an error model from a non-native English corpus, focusing on articles and prepositions. A fine-grained analysis is achieved by conditioning the errors on appropriate words in the context. Index Terms — computer-assisted language learning, secondlanguage acquisition, grammar checking 1.

In this paper we discuss our approach toward establishing a model of the acquisition of English grammatical structures by users of our English language tutoring system, which has been designed for deaf users of American Sign Language. We explore the correlation between a corpus of error-tagged texts and their holistic proficiency scores assigned by experts in order to draw initial conclusions about what language errors typically occur at different levels of proficiency in this population. Since errors made at lower levels (and not at higher levels) presumably represent constructions acquired before those on which errors are found only at higher levels, this should provide insight into the order of acquisition of English grammatical forms.

This paper explores the motivation and prerequisites of a successful integration of Intelligent Computer-Assisted Language Learning (ICALL) tools into current foreign language teaching and learning (FLTL) practice. We focus on two aspects, which we argue to be important for effective ICALL system development and use: (i) the relationship between activity design and restrictions needed to make natural language processing tractable and reliable, and (ii) pedagogical considerations and the influence of activity design choices on the integration of ICALL systems into

Abstract. 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. Key words. CALL,empirical analysis,ITS,NLP,parse disambiguation,student modeling, stereotypes 1.

In recent years, research has progressed steadily in regard to the use of computers to recognize and render sign language. This paper reviews significant projects in the field beginning with finger-spelling hands such as ‘‘Ralph’ ’ (robotics), Cyber-Gloves (virtual reality sensors to capture isolated and continuous signs), camera-based projects such as the CopyCat interactive American Sign Language game (computer vision), and sign recognition software (Hidden Markov Modeling and neural network systems). Avatars such as ‘‘Tessa’ ’ (Text and Sign Support Assistant; three-dimensional imaging) and spoken language to sign language translation systems such as Poland’s project entitled ‘‘THETOS’ ’ (Text into Sign Language Automatic Translator, which operates in Polish; natural language processing) are addressed. The application of this research to education is also explored. The ‘‘ICICLE’’ (Interactive Computer Identification and Correction of Language Errors) project, for example, uses intelligent computer-aided instruction to build a tutorial system for deaf or hard-of-hearing children that analyzes their English writing and makes tailored lessons and recommendations. Finally, the article considers synthesized sign, which is being added to educational material and has the potential to be developed by students themselves. Technology is rapidly changing and improving the way the world operates. Barriers for people who are deaf are diminishing as projects of the past two decades have unfolded. Through the use of artificial intelligence, researchers are striving to develop hardware and software that will impact the way deaf individuals communicate and learn. This paper takes the reader

In this paper, the research areas of Computer and Information

A computer conversational system can potentially help a foreignlanguage student improve his/her fluency through practice dialogues. One of its potential roles could be to correct ungrammatical sentences. This paper 1 describes our research on a sentencelevel, generation-based approach to grammar correction: first, a word lattice of candidate corrections is generated from an illformed input. A traditional n-gram language model is used to produce a small set of N-best candidates, which are then reranked by parsing using a stochastic context-free grammar. We evaluate this approach in a flight domain with simulated ill-formed sentences. We discuss its potential applications in a few related tasks. Index Terms: computer-assisted language learning, dialogue systems, natural language generation.