Exploring Automated Essay Scoring for Nonnative English Speakers

TL;DR

This paper investigates automated essay scoring tailored for nonnative English speakers, demonstrating a methodology that achieves a correlation of 0.750 with manual evaluations, addressing a gap in existing AES systems.

Contribution

It introduces a novel AES methodology specifically designed for nonnative speakers, incorporating features that capture linguistic nuances unique to this group.

Findings

Achieved a correlation coefficient of 0.750 with manual scores.

Identified features linked to nonnative language use.

Demonstrated the feasibility of automated scoring for nonnative essays.

Abstract

Automated Essay Scoring (AES) has been quite popular and is being widely used. However, lack of appropriate methodology for rating nonnative English speakers' essays has meant a lopsided advancement in this field. In this paper, we report initial results of our experiments with nonnative AES that learns from manual evaluation of nonnative essays. For this purpose, we conducted an exercise in which essays written by nonnative English speakers in test environment were rated both manually and by the automated system designed for the experiment. In the process, we experimented with a few features to learn about nuances linked to nonnative evaluation. The proposed methodology of automated essay evaluation has yielded a correlation coefficient of 0.750 with the manual evaluation.