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

Autor/inn/en	Sung, Yao-Ting; Lin, Wei-Chun; Dyson, Scott Benjamin; Chang, Kuo-En; Chen, Yu-Chia
Titel	Leveling L2 Texts through Readability: Combining Multilevel Linguistic Features with the CEFR
Quelle	In: Modern Language Journal, 99 (2015) 2, S.371-391 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0026-7902
DOI	10.1111/modl.12213
Schlagwörter	Mandarin Chinese; Second Language Learning; Readability; Reading Material Selection; Textbook Selection; Classification; Language Proficiency; Difficulty Level; Scores; Accuracy; Expertise + Suchen Sie Ihr Suchwort? Zweitsprachenerwerb; Lesbarkeit; Classification system; Klassifikation; Klassifikationssystem; Language skill; Language skills; Sprachkompetenz; Schwierigkeitsgrad; Expert appraisal
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01