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Leveling L2 Texts through Readability: Combining Multilevel Linguistic Features with the CEFR

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Title: Leveling L2 Texts through Readability: Combining Multilevel Linguistic Features with the CEFR
Language: English
Authors: Sung, Yao-Ting, Lin, Wei-Chun, Dyson, Scott Benjamin
Source: Modern Language Journal. Sum 2015 99(2):371-391.
Availability: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
Peer Reviewed: Y
Page Count: 21
Publication Date: 2015
Document Type: Journal Articles
Reports - Research
Descriptors: Mandarin Chinese, Second Language Learning, Readability, Reading Material Selection, Textbook Selection, Classification, Language Proficiency, Difficulty Level, Scores, Accuracy, Expertise
DOI: 10.1111/modl.12213
ISSN: 0026-7902
Abstract: Selecting appropriate texts for L2 (second/foreign language) learners is an important approach to enhancing motivation and, by extension, learning. There is currently no tool for classifying foreign language texts according to a language proficiency framework, which makes it difficult for students and educators to determine the precise difficulty/complexity levels of an unclassified text. Taking the Chinese language as an example, this study aimed to create a readability assessment system, called the Chinese Readability Index Explorer for Chinese as a Foreign Language (CRIE-CFL), in order to level--that is, to sort by proficiency level--texts that will be used for instructional purposes. The framework of choice in this project is the Common European Framework of Reference (CEFR). A team of expert CFL teachers first classified 1,578 CFL texts into their appropriate CEFR levels. A set of 30 CFL readability features was then developed or drawn from previous research, and sorted according to importance using F-scores. In addition, a support vector machine model was trained by sequentially integrating the features into the model to optimize accuracy. The empirical evaluation of CRIE-CFL revealed average exact- and adjacent-level accuracies of 74.97% and 99.62%, respectively, for predicting the expert classification of a text. The functionalities of CRIE-CFL are introduced and discussed.
Abstractor: As Provided
Entry Date: 2015
Accession Number: EJ1070144
Database: ERIC
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  Data: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
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  Data: Selecting appropriate texts for L2 (second/foreign language) learners is an important approach to enhancing motivation and, by extension, learning. There is currently no tool for classifying foreign language texts according to a language proficiency framework, which makes it difficult for students and educators to determine the precise difficulty/complexity levels of an unclassified text. Taking the Chinese language as an example, this study aimed to create a readability assessment system, called the Chinese Readability Index Explorer for Chinese as a Foreign Language (CRIE-CFL), in order to level--that is, to sort by proficiency level--texts that will be used for instructional purposes. The framework of choice in this project is the Common European Framework of Reference (CEFR). A team of expert CFL teachers first classified 1,578 CFL texts into their appropriate CEFR levels. A set of 30 CFL readability features was then developed or drawn from previous research, and sorted according to importance using F-scores. In addition, a support vector machine model was trained by sequentially integrating the features into the model to optimize accuracy. The empirical evaluation of CRIE-CFL revealed average exact- and adjacent-level accuracies of 74.97% and 99.62%, respectively, for predicting the expert classification of a text. The functionalities of CRIE-CFL are introduced and discussed.
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      – SubjectFull: Readability
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