Human and Automated CEFR-based Grading of Short Answers

Anaïs Tack,Sophie Roekhaut,Cédrick Fairon,Thomas François

doi:10.18653/v1/w17-5018

Abstract

This paper is concerned with the task of automatically assessing the written proficiency level of non-native (L2) learners of English. Drawing on previous research on automated L2 writing assessment following the Common European Framework of Reference for Languages (CEFR), we investigate the possibilities and difficulties of deriving the CEFR level from short answers to open-ended questions, which has not yet been subjected to numerous studies up to date. The object of our study is twofold: to examine the intricacy involved with both human and automated CEFR-based grading of short answers. On the one hand, we describe the compilation of a learner corpus of short answers graded with CEFR levels by three certified Cambridge examiners. We mainly observe that, although the shortness of the answers is reported as undermining a clear-cut evaluation, the length of the answer does not necessarily correlate with inter-examiner disagreement. On the other hand, we explore the development of a soft-voting system for the automated CEFR-based grading of short answers and draw tentative conclusions about its use in a computer-assisted testing (CAT) setting.

Highlights

The recent years have seen a growth of interest in Automated Writing Evaluation (AWE) for levelling non-native (L2) writing proficiency
Among the variety of assessment scales used, a number of studies have focused on levelling the writing proficiency following the Common European Framework of Reference (CEFR) (Council of Europe, 2001) through a combination of machine learning techniques and linguistic complexity features (Vajjala and Loo, 2014; Volodina et al, 2016a; Pilan et al, 2016)
In the context of L2 short answer grading, we mainly find systems developed for evaluating responses to reading comprehension questions, such as the CoMiC systems developed for English and German (Meurers et al, 2011)

Summary

Introduction

The recent years have seen a growth of interest in Automated Writing Evaluation (AWE) for levelling non-native (L2) writing proficiency. One application that comes to mind is the validation of the required writing skills of a large group of university students In this scenario, implementing an expert-only testing procedure is costly for two reasons. For various dimensions of proficiency (i.e. speaking, writing, etc.), it lists ‘cando’ descriptors that can be used to assign a level to a learner. These criteria have been widely used in L2 teaching and research, studies have stressed the need for more empirical research on how the different levels are linked with particular aspects of L2 proficiency (Hulstijn, 2007) (f.i. writing proficiency). The recent years have seen an increasing availability of learner corpora aligned with the CEFR

Methods

Results

Conclusion