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ICICLE System Architecture.
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In this paper we discuss an envisioned text planner for a computer-assisted instruction tool for deaf learners of English. We describe the problem of deaf literacy and overview our system, designed to act as a writing tutor that generates text to explain the errors found in a user's written compositions. These explanations are created according to...
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... is described further in following sections. Figure 1 contains a block diagram of the system under development. ICICLE is equipped with a user model for tracking the user's level of language acquisition, repre- sented by the language features the user has learned and those he is in the process of acquiring. ...
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... The sign-order rules of ASL are not the same as the word-order rules of English, and ASL syntax includes systematic modulations to signs as well as non-manual behavior (e.g., posture and facial expression) that achieve a simultaneous mode of communication not possible with the completely sequential nature of written English (Baker and Cokely, 1980). We discussed these differences plus the differences in cognitive processing techniques between spoken and manual languages in (Michaud and McCoy, 1998) and (). For ASL natives, English is a distinctly different and challenging language, motivating the need to view the process of acquiring fluency in written English as second language acquisition and to incorporate that view in a strategy for facilitating the learning process. ...
The ICICLE project is a Computer-Assisted Language Learning (CALL) environment geared toward teaching English as a second language. This paper reports on an initial prototype application of the system for deaf learners of written English. A primary focus of the ICICLE effort has been devoted to enabling the system to adapt to a learning user over the evolution of language proficiency. In this paper, we overview and motivate the design of our novel user modeling component that integrates Selinkers Interlanguage theory and other research in Second Language Acquisition in order to accurately represent a learners internal grammar as it changes over time. The objectives of this effort are two-fold: to accurately diagnose and respond to learner errors, and to focus tutorial feedback on those errors which are most relevant to the learner's acquisition process.
... (understandably) is a truly di⁄cult language for many deaf individuals to master (Charrow, 1975; Charrow and Fletcher, 1974; Charrow and Wilbur, 1975; Quigley et al., 1977; Wilbur, 1977), we view the acquisition of written English skills to be a task in second language acquisition for these learners (Michaud and McCoy, 1998;). Because some deaf individuals achieve a high level of success with English while others struggle with basic elements of the language (Padden and Ramsey, 1998; Stewart, 2001; Swisher, 1989 ), and our system endeavors to accurately address the needs of as broad a spectrum of users as possible, user adaptivity through user modeling has been a primary focus of our system development e¡orts. ...
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 disambigua- tion. 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 meth- odology 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.
... 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. ...
In this paper we discuss a proposed user knowl- edge 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.
... The envisioned system is called ICICLE (Interactive Computer Identification and Correction of Language Errors) [45], [51], [46] . It can be viewed as two interacting modules: the error identification module, which is responsible for analyzing the text and identifying errors to be corrected, and the response generation module, which is responsible for generating and presenting the tutorial dialogue. ...
This paper describes progress toward a prototype imple- mentation of a tool which aims to improve literacy in deaf high school and college students who are native (or near native) signers of American Sign Language (ASL). We envision a system that will take a piece of text written by a deaf student, analyze that text for grammatical errors, and engage that student in a tutorial dialogue, enabling the stu- dent to generate appropriate corrections to the text. A strong focus of this work is to develop a system which adapts this process to the knowledge level and learning strengths of the user and which has the flexibility to engage in multi-modal, multi-lingual tutorial instruction utilizing both English and the native language of the user.
... The correction phase of the system is a focus of current research. A description of the strategies for this phase can be found in (Michaud and McCoy, 1998) and . ...
This paper reports on the recognition component of an intelligent tutoring system that is designed to help foreign language speakers learn standard English. The system models the grammar of the learner, with this instantiation of the system tailored to signers of American Sign Language (ASL). We discuss the theoretical motivations for the system, various difficulties that have been encountered in the implementation, as well as the methods we have used to overcome these problems. Our method of capturing ungrammaticalities involves using mal-rules (also called 'error productions'). However, the straightforward addition of some mal-rules causes significant performance problems with the parser. For instance, the ASL population has a strong tendency to drop pronouns and the auxiliary verb 'to be'. Being able to account for these as sentences results in an explosion in the number of possible parses for each sentence. This explosion, left unchecked, greatly hampers the performance of the system. We discuss how this is handled by taking into account expectations from the specific population (some of which are captured in our unique user model). The different representations of lexical items at various points in the acquisition process are modeled by using mal-rules, which obviates the need for multiple lexicons. The grammar is evaluated on its ability to correctly diagnose agreement problems in actual sentences produced by ASL native speakers.
... It then generates text for tutoring the grammatical errors found in the essay. The tutorial dialogue is tailored to the student and also takes advantage of the dialogue history to encourage meaningful learning by tying new information together with recently discussed and established knowledge [16]. ICICLE is equipped with a student model for tracking the student's level of language acquisition, represented by the language features that the student has learned and those he is in the process of acquiring. ...
... ICICLE is equipped with a student model for tracking the student's level of language acquisition, represented by the language features that the student has learned and those he is in the process of acquiring. A history module to track the success or failure of various tutorial techniques with a student is also maintained [16]. ...
Intelligent Tutoring Systems (ITS) refer to computer applications that assist learners through coaching, teaching, testing, guiding exploration, and providing analysis. Though different modalities have been used to present output based on the specific domain and goals of the system, textual output remains to be predominant. Current researches in the field of ITS explore the use of text generators as the underlying model in generating tutorial text that will guide the individual student to learn concepts and solve problems in a given domain. The use of natural language to generate text usually permits the tutor to present more detailed information to the student than other modalities [9]. This paper focuses on these natural language-based ITS, which draws from several areas of computer science, including planning, text generation, knowledge representation, user models, and tutoring.
This paper presents a model for representing the proficiency of users in a CALL system by recording their performance on specific grammatical features. The model will be used both to select accurate interpretations of user-written sentences and to focus system-delivered instruction on topics at the frontier of the learner's competence. Introduction Modeling characteristics of the user in an intelligent tutoring system is an essential undertaking if the system is to adapt itself to the needs of the individual learner. It is, after all, the adaptability of a tutoring system which distinguishes it from an instructional text, whose only capability is to provide information to the learner in a predetermined manner regardless of the learner's existing knowledge or strengths. A well-designed tutoring system plays two roles: it is a diagnostician, discovering the nature and extent of the student's knowledge, and a strategist, planning a response (such as the communication of informatio...
