To predict L2 writing quality using lexical richness indices: An investigation of learners of Chinese as a Foreign Language

The majority of current lexical richness indices are based on Indo-European languages, especially English, with a lack of research that investigates their applicability to diverse language types. This study examines the applicability of 44 lexical richness indices, based on Indo-European languages, to Chinese, with a specific emphasis on investigating their predictive power for the writing quality of Chinese as a Foreign Language (CFL) learners. The present study reveals that five indices (Number of Different Words, Uber Index, Corrected Verb Variation1, Lexical Frequency Profile, and Verb Sophistication1) assessing English as a second language (L2) can also effectively predict the writing quality of CFL learners, and six indices (Verb Variation, Verb Variation1, Function Words Ratio_ auxiliary, Function Words Ratio_ pronoun, Lexical Density1, and Lexical Density2) may be more suitable for measuring Chinese, but not English. Moreover, transformed indices demonstrate a better capacity to measure L2 Chinese compared to traditional indices. In conducting a preliminary exploration of effective indices for measuring the lexical richness of Chinese interlanguage, this study emphasizes their potential impact on L2 teaching and suggests directions for future research.

Automated summary

The majority of lexical richness indices are based on Indo-European languages, particularly English, and their applicability to other language types is lacking research. This study examines the applicability of 44 such indices to Chinese and finds that certain indices can effectively predict the writing quality of Chinese as a Foreign Language (CFL) learners. The study also identifies specific indices that may be more suitable for measuring Chinese than English, and highlights the better measuring capacity of transformed indices for L2 Chinese. This study emphasizes the potential impact of effective indices on L2 teaching and suggests future research directions.