Abstract
Testing has long been acknowledged as an integral part of language teaching and learning. However, manually designing language tests is not only time consuming but also labor intensive. Recently, due to the remarkable progress of computer technology, computer-assisted item generation (CAIG) has drawn considerable attention and becomes one of the active research areas in CALL (Computer Assisted Language Learning). CAIG provides an alternative and economical way for automatic generation of questions in relatively short time, effective establishment of item banks in large scale, and support for adaptive testing for incremental language learning. Previous work has explored generations of reading comprehension, vocabulary, listening dictation tests, but very little has been done on grammar tests. The purpose of this thesis is to address the issue of the computer-aided creation of English grammar tests. We introduce a method for the semi-automatic generation of grammar test items by applying Natural Language Processing (NLP) techniques. Based on manually-designed patterns, sentences gathered from the Web are transformed into tests on grammaticality. The method involves representing test writing knowledge as test patterns, acquiring authentic sentences on the Web, and applying generation strategies to transform sentences into items. At runtime, sentences are converted into two types of TOEFL-style question: multiple-choice and error detection. We also describe a prototype system FAST (Free Assessment of Structural Tests). Evaluation on a set of generated questions indicates that the proposed method performs satisfactorily both in item facility and item discrimination. Our methodology provides a promising approach and offers significant potential for computer assisted language learning and assessment.