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

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.