Towards Evaluating Narrative Quality In Student Writing
Open accessAbstract
This work lays the foundation for automated assessments of narrative quality in student writing. We first manually score essays for narrative-relevant traits and sub-traits, and measure inter-annotator agreement. We then explore linguistic features that are indicative of good narrative writing and use them to build an automated scoring system. Experiments show that our features are more effective in scoring specific aspects of narrative quality than a state-of-the-art feature set.
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Discourse markers can be used to measure the coherence quality of a text.
“Discourse markers can be used to measure the coherence quality of a text.”
From A large-scale comparison of human-written versus ChatGPT-generated essays (2023) · cites this paper as [53]
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- Authors
- Somasundaran, S., Flor, M., Chodorow, M., Molloy, H., Gyawali, B., & McCulla, L.
- Affiliations
- Educational Testing Service (US)
- The Graduate Center, CUNY (US)
- Corresponding author
- Somasundaran, Swapna
- Flor, Michael
- Chodorow, Martin
- Molloy, Hillary
- Gyawali, Binod
- McCulla, Laura
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press
- Published
- 2018-12
- Volume
- 6
- Pages
- 91-106
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W2787991549
- Cited by
- 29
- References
- 81
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 4 · 2022: 6 · 2021: 2 · 2020: 5 · 2019: 4
- FWCI
- 1.54
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Transactions of the Association for Computational Linguistics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Education › Writing and Handwriting Education
- Keywords
- narrative quality, student writing, automated essay scoring, linguistic features, inter-annotator agreement, writing quality assessment
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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