Analyzing the Readability of Text for Chinese as Foreign Language Learners

Readability has been of long-standing research interest to educational psychologists, it has well established that texts with high readability facilitate comprehension and learning efficiency. However, few readability studies focus on Chinese or texts that designed for learner of Chinese as foreign language. Sung (2015) proposed an approach for constructing and validating readability formulae by integrating multilevel linguistic features with the machine learning(support vector machine, SVM) model, and developed a tool for the automated analysis of Chinese texts called the Chinese Readability Index Explorer for Chinese as a Foreign Language (CRIE-CFL). The CRIE-CFL provides linguistic information, readability-level prediction, and writing diagnosis using multilevel linguistic features as predictors and proficiency level of texts that match CEFR classified by expert teachers as criterion. The predicting accuracy of CRIE-CFL is 89.86 %. This study used 597 texts from current text books published in Taiwan, using CRI-CFL as a tool for text analysis. The results suggest that only some textbooks can be categorically distinguished in terms of the readability of texts. Furthermore, the content of the textbooks pre-determined by the text authors do not concur with the target readers’ proficiency levels. In other words, the predefined reading levels of the textbooks mismatch the intended audience’s reading proficiency.

Keywords

readability text leveling Chinese as foreign language linguistic feature

source

International Journal of Chinese Language Education; June 2017; Issue No. 1; p.39 - 71

Publisher

Department of Chinese Language Studies, The Education University of Hong Kong
Chinese Language Program, Columbia University
Chung Hwa Book Co. (H.K.) Ltd.

Language

Chinese

ISSN

2520-7733 (Print); 2521-4241 (Online)