---
title: "Annotating Errors in English Learners' Written Language"
description: "The authors propose an annotation framework that records, for each learner error, an error type, its… 29 references, cited by 2 papers here."
authors: ["Steven Coyne", "Diana Galvan-Sosa", "Ryan Spring", "Camelia Guerraoui", "Michael Zock", "Keisuke Sakaguchi", "Kentaro Inui"]
year: 2025
venue: "arXiv:2508.06810 · AIED LNCS 15880 (2025) 292-306"
source: "https://arxiv.org/abs/2508.06810"
doi: "10.1007/978-3-031-98459-4_21"
arxiv: "2508.06810"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/coyne-2025-annotating-errors-english-learners-written/"
---
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# Annotating Errors in English Learners' Written Language Production: Advancing Automated Written Feedback Systems

**Authors:** Steven Coyne, Diana Galvan-Sosa, [Ryan Spring](https://md.research.elamur.ai/free/en/authors/ryan-spring/), Camelia Guerraoui, Michael Zock, [Keisuke Sakaguchi](https://md.research.elamur.ai/free/en/authors/keisuke-sakaguchi/) and [Kentaro Inui](https://md.research.elamur.ai/free/en/authors/kentaro-inui/) · **Year:** 2025 · **Venue:** [arXiv:2508.06810 · AIED LNCS 15,880 (2025) 292-306](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/) ([Lecture notes in computer science 2025](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/2025/)) · **Publisher:** [Springer Science+Business Media](https://md.research.elamur.ai/free/en/publishers/springer-science-business-media/) · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://doi.org/10.1007/978-3-031-98459-4_21) · [PDF](https://arxiv.org/pdf/2508.06810) · [DOI 10.1007/978-3-031-98459-4\_21](https://doi.org/10.1007/978-3-031-98459-4_21) · [arXiv 2508.06810](https://arxiv.org/abs/2508.06810) · [OpenAlex](https://openalex.org/W4412874035)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [2](https://md.research.elamur.ai/free/en/papers/coyne-2025-annotating-errors-english-learners-written/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Automated writing evaluation tools fix grammar well, but they mostly offer one-click direct edits. Language learners often gain more from short explanations or indirect hints, especially when an error breaks a general rule. The authors propose an annotation framework that records, for each learner error, an error type, its generalizability ("treatability"), a highlighted comment span, and a human-written feedback comment marked as either a direct correction or a hint. The new hierarchical typology classifies errors by the knowledge gap a teacher would infer behind them, not by the surface edit. Using the annotated data, they compare three LLM feedback generators: keyword-guided (using their tags, ERRANT, or EXPECT), keyword-free, and template-guided. Practicing teachers rated the outputs. All systems scored well, roughly on par with human writers, and the choice of tag set made little difference. Only the template system came close to the human mix of hints and corrections, but it degraded when no template fit. The work matters because it supports feedback that targets learning rather than just correcting text.

## Key points
- Typology: six collections (e.g., "Vocabulary", "Grammar") with nested tags and 81 terminal tags in total. Tag names follow textbook-style teaching concepts, so they can be linked to learning resources. Guidelines and resources are released on GitHub (coynestevencharles/annotating-errors-wcf).
- Motivation from prior work (Ferris, Hyland & Hyland): teachers corrected untreatable errors directly 65.3% of the time. For treatable errors, 58.7% of feedback was indirect.
- Teacher ratings:
  - Overall quality: about 4.45–4.50 out of 5 for the human, ERRANT, EXPECT, own-tag and no-tag sources, versus 4.184 for templates.
  - Factuality: never reached 100%, and was lowest for templates (0.921).
- Template system:
  - Picked the correct template in 76.65% of cases (F1 0.76).
  - Recall for the "None" (withhold feedback) choice was only 0.37.
  - 4.57% of outputs contained leftover template syntax.
  - Mean quality was 4.40 with a correct template, 3.51 with a wrong one, and 2.88 when it filled a template where "None" was correct.
- Directness: humans gave hints in 40.86% of cases. Keyword-based and keyword-free LLMs gave hints in only 0–3%, despite prompting. The template system gave hints in 39.77% (hint F1 0.78).

## Interesting ideas
- Label errors by the underlying construction the writer failed at, not by the surface edit: "missing preposition" splits into phrasal verb, prepositional phrase, and so on, so the label says what knowledge is missing.
- Mark each error as rule-governed or idiosyncratic/lexical, and use that flag to choose a different strategy for each case.
- Extend an annotated span beyond the minimal edit so it covers the whole lexical unit (highlighting "put down", not just "down"), because the explanation concerns the unit as a whole.
- Template-filled generation gives fine control over output style, but it needs an explicit abstain option, robust detection of coverage gaps, and an automatic check for unfilled template syntax.
- A no-tag baseline revealed that the supplied labels barely changed output quality, which is a cheap test of whether conditioning information is actually used.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): Self-induced repair rules and parser-disagreement logs could be tagged by the UD construction involved, making failure clusters explainable and easier to target.
- **I2** (learn): When a repair or correction is learned, a treatability-style flag could decide whether it becomes a general rule or stays a lexical exception, reducing overgeneralization.
- **I3** (tree): Phrasal verbs and other multiword units (compound:prt, fixed) could be treated as one span in diagnostics and frame lookup, instead of as separate tokens.
- **I4** (back): Graph-to-text templates should include a first-class "no fitting template" outcome and a post-filter, rather than forcing the nearest template.
- **I5** (learn): Run each LLM-teacher tier with and without the structured hints (tree, frames) as a negative control, to verify that the model really uses them.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Steven Coyne, Diana Galvan-Sosa, Ryan Spring, Camelia Guerraoui, Michael Zock, Keisuke Sakaguchi and Kentaro Inui. 2025. Annotating Errors in English Learners' Written Language Production: Advancing Automated Written Feedback Systems. arXiv:2508.06810 · AIED LNCS 15,880 (2025) 292-306.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{coyne2025annotating,
  title = {{Annotating Errors in English Learners' Written Language Production: Advancing Automated Written Feedback Systems}},
  author = {Steven Coyne, Diana Galvan-Sosa, Ryan Spring, Camelia Guerraoui, Michael Zock, Keisuke Sakaguchi and Kentaro Inui},
  year = {2025},
  doi = {10.1007/978-3-031-98459-4\_21},
  eprint = {2508.06810},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1007/978-3-031-98459-4_21}
}
```

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*Updated 31 October 2026*