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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
Affiliation:
Université Rennes 2, France (thomas.gaillat@univ-rennes2.fr)
Andrew Simpkin
Affiliation:
School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway (andrew.simpkin@insight-centre.org)
Nicolas Ballier
Affiliation:
Université de Paris, France (nicolas.ballier@univ-paris.fr)
Bernardo Stearns
Affiliation:
Data Science Institute (DSI), National University of Ireland, Galway (bernardo.stearns@insight-centre.org)
Annanda Sousa
Affiliation:
Data Science Institute (DSI), National University of Ireland, Galway (annanda.sousa@insight-centre.org)
Manon Bouyé
Affiliation:
Université de Paris, France (manon.bouye@etu.u-paris.fr)
Manel Zarrouk
Affiliation:
Université Sorbonne Paris Nord, France (zarrouk@lipn.univ-paris13.fr)

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).

Type
Research Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of European Association for Computer Assisted Language Learning

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