Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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

Predicting CEFR levels in learners of English: The use of microsystem criterial features in a... 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). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ReCALL Cambridge University Press

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

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

ReCALL , Volume 34 (2): 17 – May 1, 2022

Abstract

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).

Loading next page...
 
/lp/cambridge-university-press/predicting-cefr-levels-in-learners-of-english-the-use-of-microsystem-LzOCd0WpNW
Publisher
Cambridge University Press
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of European Association for Computer Assisted Language Learning
ISSN
1474-0109
eISSN
0958-3440
DOI
10.1017/S095834402100029X
Publisher site
See Article on Publisher Site

Abstract

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).

Journal

ReCALLCambridge University Press

Published: May 1, 2022

Keywords: microsystem; criterial features; supervised learning; language functions; automatic essay scoring; linguistic complexity

References