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Statistical level checker with personalised English passage suggestion
Author(s)
Chai, Wasan Na
Ruangrajitpakorn, Taneth
Hiransakolwong, Nualsawat
Supnithi, Thepchai
Date Issued
December 1, 2011
Type
Conference Paper
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.
Citation
Proceedings of the 19th International Conference on Computers in Education Icce 2011, 9-17, 2011