A refined and concise model of indices for quantitatively measuring lexical richness of Chinese university students’ EFL writing

Oct 15, 2026

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A refined and concise model of indices is proposed to measure lexical richness (LR) in Chinese university students’ EFL writing. Existing work is criticized for redundant indices and inconsistent usage, which makes it difficult to select indices that sensitively distinguish grade levels. From a learner corpus, 180 compositions were scored with 28 LR indices using Lexical Complexity Analyzer, MATTR, and Coh-Metrix. Significant ANOVA differences and effect sizes selected six indices for the model.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Article info

Abstract

Keywords

Introduction

Definition and dimensions of lexical richness

Problems of existing indices and measures

Controversies and inconsistent calculation methods

OPEN ACCESS Research Article Contemporary Educational Technology, 2024, 16(3), ep513 ISSN: 1309-517X (Online) A refined and concise model of indices for quantitatively measuring lexical richness of Chinese university students’EFL writing Yang Yang 1,2* 0000-0003-3114-0682 Ze Zheng 3 0009-0006-3085-7453 1 College of International Studies, Southwest University, Chongqing, CHINA 2 Faculty of Modern Languages and Communication, Universiti Putra Malaysia, Serdang, Selangor, MALAYSIA 3 Independent Researcher, CHINA * [Corresponding author:](Corresponding author: yangvictoryang@swu.edu.cn)[ yangvictoryang@swu.edu.cn](Corresponding author: yangvictoryang@swu.edu.cn) Citation: Yang, Y., & Zheng, Z. (2024) . A refined and concise model of indices for quantitatively measuring lexical richness of Chinese university students’ EFL writing. Contemporary Educational Technology, 16(3), ep513 . [https://doi.org/10.30935/cedtech/14707](https://doi.org/10.30935/cedtech/14707) ARTICLE INFO ABSTRACT | Received: 5 Jan 2024
Accepted: 30 May 2024 | In the existing literature, scholars have proposed various indices to measure the lexical richness (LR) of English as a foreign language (EFL) writing. However, there are currently issues of redundant indices and inconsistent usage. Attempting to address the research question of which indices are the most sensitive and effective ones to distinguish between different grade levels of Chinese university students’ EFL writing, this study aims to put forward a refined and concise model of indices that can truthfully reflect LR in EFL writing. A total of 180 compositions were selected from a Chinese EFL learner corpus: Spoken and written English corpus of Chinese learners. Scores of 28 LR indices of these compositions were computed using the software Lexical Complexity Analyzer, MATTR, and Coh-Metrix. One-way ANOVA or Welch’s ANOVA, depending on the variable’s homogeneity of variances, was conducted for each index. Two criteria were applied to determine which index of a measure should be included in the refined model: whether the difference of an index is significant among different grade levels and the effect size of ANOVA. Based on the quantitative results of ANOVAs and qualitative human judgment based on literature, six indices of the six LR measures were included in the refined model: lexical density, lexical sophistication-I, verb sophistication-II, number of different words-expected sequence 50, corrected TTR, and squared verb variation-I. This refined model addresses the issues of redundancy and inconsistency in previous studies, providing a more accurate and efficient tool for assessing LR in EFL writing.
Keywords: refined model, lexical richness, index, EFL writing, training and testing | | --- | --- | | INTRODUCTION | | Lexical richness (LR) refers to the extent of sophistication in the productive vocabulary of a language user or learner. Evaluating writing proficiency through it has been widely regarded as highly effective (Fan et al., 2023) . Within the framework of English as a foreign language (EFL) teaching and learning, LR is regarded asan important indicator of EFL proficiency (Malvern & Richards, 2013) . Regarding the connection between LRand the quality of EFL writing, many researchers (e.g., Geng & Yang, 2021; Kojima & Yamashita, 2014; TreffersDaller et al., 2018; Xie & Shen, 2015) have reported that there is a significant correlation between them. Traditionally, there are four dimensions of LR: lexical originality, density, sophistication, and variation (Gregori-Signes & Clavel-Arroitia, 2015) . Besides, lexical errors have also been regarded as another dimension of LR and it was claimed that measuring LR should consider lexical variation including errors, lexical variation excluding errors, and the ratio of lexical errors (Zhang et al., 2021) . Read (2000) claimed that good writing should have the following characteristics: (1) a relatively high

FAQs

What problem does the paper address about lexical richness (LR) indices?

The paper targets redundancy and inconsistent usage of LR indices in prior studies, which limits the ability to select sensitive and effective measures.

How were the LR indices computed and analyzed in the study?

Scores for 28 LR indices were computed using Lexical Complexity Analyzer, MATTR, and Coh-Metrix, and one-way ANOVA or Welch’s ANOVA was applied depending on variance homogeneity.

Which indices were included in the refined concise model?

The refined model includes lexical density, lexical sophistication-I, verb sophistication-II, number of different words-expected sequence 50, corrected TTR, and squared verb variation-I.

Oct 15, 2026

Uploaded by OmBimo