DREsS

Dataset for Rubric-based Essay Scoring

🎉 DREsS is accepted to ACL 2025!

Haneul Yoo

Jieun Han

So-Yeon Ahn

Alice Oh

This is an official website of DREsS: Dataset for Rubric-based Essay Scoring on EFL Writing (Yoo et al., 2025).

DREsS is a large-scale, standard dataset for rubric-based automated essay scoring. DREsS comprises three sub-datasets:

• DREsS_New with 2,279 essays from English as a foreign language (EFL) learners and their scores assessed by experts
• DREsS_Std. with 6,515 essays and scores from existing datasets
• DREsS_CASE with 40,185 synthetic essay samples.

Dataset

The essays in DREsS are scored on a range of 1 to 5, with increments of 0.5, based on the three rubrics: content, organization, and language.

Criteria	Description
Content	Paragraph is well-developed and relevant to the argument, supported with strong reasons and examples.
Organization	The argument is very effectively structured and developed, making it easy for the reader to follow the ideas and understand how the writer is building the argument. Paragraphs use coherence devices effectively while focusing on a single main idea.
Language	The writing displays sophisticated control of a wide range of vocabulary and collocations. The essay follows grammar and usage rules throughout the paper. Spelling and punctuation are correct throughout the paper.

Data attributes

Column	Type	Description
id	Integer	A unique identifier of each essay sample
source	String	[Optional] An original source of the essay sample (only for DREsS_std)
prompt	String	An essay prompt
essay	String	A student-written essay
score	Float	A rubric-based score of the essay (content, organization, language, total)

Data statistics

Subdata	Source	Content	Organization	Language
DREsS_New	-	2,279	2,279	2,279
DREsS_Std.	ASAP P7	1,569	1,569	1,569
	ASAP P8	723	723	723
	ASAP++ P1	1,785	1,785	1,785
	ASAP++ P2	1,799	1,799	1,799
	ICNALE EE	639	639	693
DREsS_CASE	-	8,307	31,086	792
Total		17,101	39,880	9,586

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Citation

@inproceedings{yoo-etal-2025-dress,
    title = "{DRE}s{S}: Dataset for Rubric-based Essay Scoring on {EFL} Writing",
    author = "Yoo, Haneul  and
      Han, Jieun  and
      Ahn, So-Yeon  and
      Oh, Alice",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-long.659/",
    doi = "10.18653/v1/2025.acl-long.659",
    pages = "13439--13454",
    ISBN = "979-8-89176-251-0",
}