AsiaTEFL Logo        The Journal of AsiaTEFL
   
The Journal of AsiaTEFL
Articles By Subject
Current Issue
Past Issues
Special Issue
Information of the Journal
Editorial Board
Submission Guidelines
Ethical Guidelines
Manuscript Submission
Journal Order
User Information
Search
Today 1,111
Total 653,842
Current Issue
Go List

Volume 16 Number 1, Spring 2019, Pages 1-447   


PDF Download
   

Predicting Second Language Writing Proficiency in Learner Texts Using Computational Tools

    YeonJoo Jung, Scott Crossley & Danielle McNamara


This study explores whether linguistic features can predict second language writing proficiency in the Michigan English Language Assessment Battery (MELAB) writing tasks. Advanced computational tools were used to automatically assess linguistic features related to lexical sophistication, syntactic complexity, cohesion, and text structure of writing samples graded by expert raters. The findings of this study show that an analysis of linguistic features can be used to significantly predict human judgments of the essays for the MELAB writing tasks. Furthermore, the findings indicate the relative contribution of a range of linguistic features in MELAB essays to overall second language (L2) writing proficiency scores. For instance, linguistic features associated with text length and lexical sophistication were found to be more predictive of writing quality in MELAB than those associated with cohesion and syntactic complexity. This study has important implications for defining writing proficiency at different levels of achievement in L2 academic writing as well as improving the current MELAB rating scale and rater training practices. Directions for future research are also discussed.

Keywords: linguistic features, second language writing proficiency, computational analysis