TY - JOUR
T1 - Leveling L2 Texts Through Readability
T2 - Combining Multilevel Linguistic Features with the CEFR
AU - Sung, Yao-Ting
AU - Lin, Wei Chun
AU - Dyson, Scott Benjamin
AU - Chang, Kuo-En
AU - Chen, Yu Chia
PY - 2015/6/1
Y1 - 2015/6/1
N2 - 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.
AB - 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.
KW - CEFR
KW - CRIE-CFL
KW - Leveling
KW - Mandarin Chinese
KW - Readability
UR - http://www.scopus.com/inward/record.url?scp=84937907278&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937907278&partnerID=8YFLogxK
U2 - 10.1111/modl.12213
DO - 10.1111/modl.12213
M3 - Article
AN - SCOPUS:84937907278
VL - 99
SP - 371
EP - 391
JO - Modern Language Journal
JF - Modern Language Journal
SN - 0026-7902
IS - 2
ER -