Artificial Intelligence techniques are increasingly being used to enhance the area of Computer-Aided Instruction. This thesis is concerned with the area of Computer-Aided Language Learning, a subset of Computer-Aided Instruction, and demonstrates how various Artificial Intelligence techniques can be incorporated into a language system to produce an intelligent educational tool. In this thesis, the focus is on the use of English articles, which is a subtle area of the English language with which even advanced students of English have difficulty. This thesis describes <i>Artcheck</i>, an intelligent Computer-Aided Language Learning (ICALL) system which detects, analyses and responds to English article usage errors. This system has three main features: it has knowledge of the article usage domain; it dynamically creates a <i>model</i> of the student; and it <i>adapts</i> to the individual student. The system's <i>knowledge</i> of the domain consists of a set of article usage rules which reflect standard teaching practice. The information necessary to apply the rules is extracted at the natural language processing stage, and includes structural and contextual information. The system <i>models</i> the state of the student's knowledge at all times, in order to give informative explanations to the student about any errors which are made. It is able to generate mal-rules which account for consistent errors made by the student, using <i>version spaces</i> and the <i>candidate elimination algorithm</i>. The student model can be described as <i>dynamic</i> because the generation of mal-rules can create new parts of the student model, in response to student behaviour, which are not pre-determined by the system designer. The system <i>responds</i> to individual students by giving explanations of errors which are tailored to the student's level of ability and preferred learning style.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:661745 |
| Date | January 1993 |
| Creators | Sentance, Susan |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/20176 |
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