Exploring Automated Essay Scoring for Nonnative English Speakers
This addresses a gap in automated essay scoring for nonnative speakers, though it appears incremental as it builds on existing AES methods.
The paper tackled the problem of automated essay scoring for nonnative English speakers by developing a methodology that learns from manual evaluations, achieving a correlation coefficient of 0.750 with manual ratings.
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.