SCALEFeedback：面向 LLM 教育反馈研究的大规模合成计算机作业数据集
SCALEFeedback: A Large-Scale Dataset of Synthetic Computer Science Assignments for LLM-generated Educational Feedback Research
研究者发布 SCALEFeedback，一个面向 LLM 教育反馈研究的开源合成计算机作业数据集，含 59 门大学计算机课程、155 份作业的 10,000 份合成学生提交。数据集由 Sophisticated Assignment Mimicry（SAM）框架基于真实作业描述、评分标准和学生提交做一对一 LLM 模仿生成，技术验证显示其与真实数据高度接近且已去除个人身份信息。
Authors:Keyang Qian, Kaixun Yang, Wei Dai, Flora Jin, Yixin Cheng, Rui Guan, Sadia Nawaz, Zachari Swiecki, Guanliang Chen, Lixiang Yan, Dragan Gašević
Abstract:Using Large Language Models (LLMs) to give educational feedback to students for their assignments has attracted much attention in the AI in Education (AIED) field. Yet, there is currently no large-scale open-source dataset of student assignments that includes detailed assignment descriptions, rubrics, and student submissions across various courses. As a result, research on generalisable methodology for automatic generation of effective and responsible educational feedback remains limited. In this paper, we introduce a synthetic computer science university assignment dataset for LLM-based educational feedback research, called SCALEFeedback (Synthetic Computer science Assignments for LLM Educational Feedback Research). The dataset is generated via Sophisticated Assignment Mimicry (SAM) framework specifically designed to synthesise this dataset and that utilizes one-to-one LLM-based imitation from real assignment descriptions, rubrics, and student submissions. Our open-source dataset contains 10,000 synthetic student submissions spanning 155 assignments across 59 university-level computer science courses. Technical validation confirmed that the synthetic dataset closely resembles real data while successfully eliminating personally identifiable information present in the source material. The creation of this dataset is a valuable contribution to researchers who aim to develop LLM-based generalisable methods for offering high-quality, automated educational feedback in a scalable way.
| Subjects: | Computers and Society (cs.CY) |
| Cite as: | arXiv:2508.05953 [cs.CY] |
| (or arXiv:2508.05953v3 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2508.05953 arXiv-issued DOI via DataCite |
Submission history
From: Keyang Qian Mr. [view email]
[v1]
Fri, 8 Aug 2025 02:37:20 UTC (5,605 KB)
[v2]
Wed, 1 Jul 2026 20:34:44 UTC (5,534 KB)
[v3]
Mon, 26 Oct 2026 09:21:28 UTC (5,511 KB)
来源：arXiv cs.CY · arxiv.org