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Predicting CEFR levels in learners of English: The use of microsystem criterial features in a machine learning approach

Published online by Cambridge University Press:  10 November 2021

Thomas Gaillat Open the ORCID record for Thomas Gaillat [Opens in a new window] ,
Andrew Simpkin Open the ORCID record for Andrew Simpkin [Opens in a new window] ,
Nicolas Ballier Open the ORCID record for Nicolas Ballier [Opens in a new window] ,
Bernardo Stearns Open the ORCID record for Bernardo Stearns [Opens in a new window] ,
Annanda Sousa Open the ORCID record for Annanda Sousa [Opens in a new window] ,
Manon Bouyé Open the ORCID record for Manon Bouyé [Opens in a new window]  and
Manel Zarrouk Open the ORCID record for Manel Zarrouk [Opens in a new window]
Thomas Gaillat
Affiliation:
Université Rennes 2, France ([email protected])
Andrew Simpkin
Affiliation:
School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway ([email protected])
Nicolas Ballier
Affiliation:
Université de Paris, France ([email protected])
Bernardo Stearns
Affiliation:
Data Science Institute (DSI), National University of Ireland, Galway ([email protected])
Annanda Sousa
Affiliation:
Data Science Institute (DSI), National University of Ireland, Galway ([email protected])
Manon Bouyé
Affiliation:
Université de Paris, France ([email protected])
Manel Zarrouk
Affiliation:
Université Sorbonne Paris Nord, France ([email protected])
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Abstract

This paper focuses on automatically assessing language proficiency levels according to linguistic complexity in learner English. We implement a supervised learning approach as part of an automatic essay scoring system. The objective is to uncover Common European Framework of Reference for Languages (CEFR) criterial features in writings by learners of English as a foreign language. Our method relies on the concept of microsystems with features related to learner-specific linguistic systems in which several forms operate paradigmatically. Results on internal data show that different microsystems help classify writings from A1 to C2 levels (82% balanced accuracy). Overall results on external data show that a combination of lexical, syntactic, cohesive and accuracy features yields the most efficient classification across several corpora (59.2% balanced accuracy).

Keywords

microsystemcriterial featuressupervised learninglanguage functionsautomatic essay scoringlinguistic complexity
Type
Research Article
Information
ReCALL , Volume 34 , Issue 2 , May 2022 , pp. 130 - 146
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Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of European Association for Computer Assisted Language Learning

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