Preprint
SEFORA: Student Essays with Feedback Corpus and LLM Feedback Evaluation Framework
ArXiv.org
arXiv
06/30/2026
DOI: 10.48550/arxiv.2607.00274
Abstract
Effective writing feedback is among the strongest drivers of student learning, yet producing it at scale is labor-intensive. LLMs offer a natural path to scaling writing support, but two gaps stand in the way: few public corpora capture how instructors actually deliver feedback in real classrooms, and no reliable method measures whether generated feedback aligns with what an instructor would write. We address both. SEFORA is a public corpus pairing instructor inline feedback with assignment prompts, rubrics, scores, and multi-draft revisions across various college writing genres, comprising 564 drafts and 8,240 instructor annotations. UniMatch is a reference-based evaluation framework for open-ended generation: it segments feedback into feedback units, scores their semantic correspondence under instructor-derived criteria, and aligns them via optimal matching to yield interpretable precision, recall, and F1. Across 74 experimental configurations spanning multiple LLMs, no setting exceeds 0.4 F1. UniMatch reveals that models struggle to identify the feedback instructors would prioritize, and performance degrades as models generate more.
Details
- Title: Subtitle
- SEFORA: Student Essays with Feedback Corpus and LLM Feedback Evaluation Framework
- Creators
- Shayan Peyghambari OskouiNorah AlmousaZhaoyi Joey HouCarolina GustafsonGayle RogersRaquel CoelhoDiane LitmanXiang Lorraine Li
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2607.00274
- ISSN
- 2331-8422
- Publisher
- arXiv
- Language
- English
- Date posted
- 06/30/2026
- Academic Unit
- Nursing
- Record Identifier
- 9985179092602771
Metrics
1 Record Views