Statistical Level Checker with Personalised English Passage Suggestion

Abstract

In this paper, a system to classify a readability level of English reading passage and to match student personal interest is purposed. Student model is applied to collect student information for selecting their preferable passage topic. Statistical passage level checker is implemented to match student readability level with passage difficulty by using neural network. Three linguistic features, syllable, vocabulary and sentence complexity, are chosen to distinguish a difficulty difference among passage level. The best accuracy gained by the system is 86.25% and the constantly reliable feature for this task is a sentence complexity of the passage.