Year: 2025
Type: conference-paper
Abstract: Automated essay scoring (AES) is a useful tool in English as a Foreign Language (EFL) writing education, offering real-time essay scores for students and instructors.However, previous AES models were trained on essays and scores irrelevant to the practical scenarios of EFL writing education and usually provided a single holistic score due to the lack of appropriate datasets.In this paper, we release DREsS, a large-scale, standard dataset for rubric-based automated essay scoring with 48.9K samples in total.DREsS comprises three sub-datasets: DREsS New , DREsS Std. , and DREsS CASE .We collect DREsS New , a real-classroom dataset with 2.3K essays authored by EFL undergraduate students and scored by English education experts.We also standardize existing rubricbased essay scoring datasets as DREsS Std. .We suggest CASE, a corruption-based augmentation strategy for essays, which generates 40.1K synthetic samples of DREsS CASE and improves the baseline results by 45.44%.DREsS will enable further research to provide a more accurate and practical AES system for EFL writing education.
Authors: Haneul Yoo, Jieun Han, So-Yeon Ahn, Alice Oh
Institution: Korea Advanced Institute of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4412945267