Exploring the cross-lingual influence of linguistic complexity in second language writing assessment

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3ASWMZT5UG" target="_blank" >RIV/00216208:11320/26:SWMZT5UG - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.asw.2025.100951" target="_blank" >http://dx.doi.org/10.1016/j.asw.2025.100951</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asw.2025.100951" target="_blank" >10.1016/j.asw.2025.100951</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Exploring the cross-lingual influence of linguistic complexity in second language writing assessment

Popis výsledku v původním jazyce

This paper explores the influence of L1 on the linguistic complexity of English learners. It relies on features extracted from texts and modelled using a statistical learning framework. Linguistic complexity is assessed automatically in terms of proficiency levels across different L1. We investigate whether proficiency grading by humans matches clusters of learner writings based on the similarity of linguistic features. We then use complexity metrics to automatically assess proficiency levels in samples of writings of different L1s. We focus on variable importance to understand which features best discriminate between levels. Analytic clusters of linguistic complexity data do not map well to learning levels, which promises poorly for the relevance of using language complexity metrics for level prediction. However, assessing L1 influence on linguistic complexity through a multinomial logistic regression with elastic net regularisation shows significant results. The models predict the proficiency levels of students of different L1s. © 2025

Název v anglickém jazyce

Exploring the cross-lingual influence of linguistic complexity in second language writing assessment

Popis výsledku anglicky

This paper explores the influence of L1 on the linguistic complexity of English learners. It relies on features extracted from texts and modelled using a statistical learning framework. Linguistic complexity is assessed automatically in terms of proficiency levels across different L1. We investigate whether proficiency grading by humans matches clusters of learner writings based on the similarity of linguistic features. We then use complexity metrics to automatically assess proficiency levels in samples of writings of different L1s. We focus on variable importance to understand which features best discriminate between levels. Analytic clusters of linguistic complexity data do not map well to learning levels, which promises poorly for the relevance of using language complexity metrics for level prediction. However, assessing L1 influence on linguistic complexity through a multinomial logistic regression with elastic net regularisation shows significant results. The models predict the proficiency levels of students of different L1s. © 2025

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Assessing Writing

ISSN

1075-2935

e-ISSN

—

Svazek periodika

66

Číslo periodika v rámci svazku

2025

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

100951

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105013193449