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Automated Run-On Sentence Detection and Correction for Educational Writing

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Title: Automated Run-On Sentence Detection and Correction for Educational Writing
Language: English
Authors: Shubham Chakraborty, Yu Tian, Michelle Banawan, Andrew Potter (ORCID 0000-0002-1012-2680), Linh Huynh (ORCID 0000-0002-5387-4137), Yoshita Yajjapurapu, Danielle S. McNamara
Source: Grantee Submission. 2026.
Peer Reviewed: Y
Page Count: 5
Publication Date: 2026
Sponsoring Agency: National Center for Education Research (NCER) (ED/IES)
Contract Number: R305T240035
R305N210041
Document Type: Speeches/Meeting Papers
Reports - Research
Descriptors: Writing Evaluation, Natural Language Processing, Artificial Intelligence, Sentence Structure, Grammar, Error Correction, Automation, Student Writing Models, Instructional Design
Abstract: Run-on sentences, including fused sentences, comma splices, and conjunctive adverb misuse, pose a persistent challenge in student writing, undermining both human evaluation and automated analyses in learning environments. Despite their instructional importance, run-ons are underrepresented in major grammatical error correction (GEC) benchmarks. We present a two-stage NLP pipeline for run-on detection and minimal-change correction, designed within the Learning Engineering Framework to improve writing feedback while preserving student voice. Early annotation of 251 student sentences identified 29 potential run-ons, informing our pipeline design and human validation workflows. [This paper was published in: "Proceedings of the Learning Engineering Research Network Convening (LERN 2026)," 2026, pp. 160-163.]
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2026
Access URL: https://edtecharchives.org/conference_proceeding/2551/25361
Accession Number: ED678820
Database: ERIC
FullText Text:
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  Data: Automated Run-On Sentence Detection and Correction for Educational Writing
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  Data: <searchLink fieldCode="AR" term="%22Shubham+Chakraborty%22">Shubham Chakraborty</searchLink><br /><searchLink fieldCode="AR" term="%22Yu+Tian%22">Yu Tian</searchLink><br /><searchLink fieldCode="AR" term="%22Michelle+Banawan%22">Michelle Banawan</searchLink><br /><searchLink fieldCode="AR" term="%22Andrew+Potter%22">Andrew Potter</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1012-2680">0000-0002-1012-2680</externalLink>)<br /><searchLink fieldCode="AR" term="%22Linh+Huynh%22">Linh Huynh</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5387-4137">0000-0002-5387-4137</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yoshita+Yajjapurapu%22">Yoshita Yajjapurapu</searchLink><br /><searchLink fieldCode="AR" term="%22Danielle+S%2E+McNamara%22">Danielle S. McNamara</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Grantee+Submission%22"><i>Grantee Submission</i></searchLink>. 2026.
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  Data: 5
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  Data: 2026
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  Data: National Center for Education Research (NCER) (ED/IES)
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  Data: R305T240035<br />R305N210041
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  Data: Speeches/Meeting Papers<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Writing+Evaluation%22">Writing Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+Language+Processing%22">Natural Language Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Sentence+Structure%22">Sentence Structure</searchLink><br /><searchLink fieldCode="DE" term="%22Grammar%22">Grammar</searchLink><br /><searchLink fieldCode="DE" term="%22Error+Correction%22">Error Correction</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Writing+Models%22">Student Writing Models</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Design%22">Instructional Design</searchLink>
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  Data: Run-on sentences, including fused sentences, comma splices, and conjunctive adverb misuse, pose a persistent challenge in student writing, undermining both human evaluation and automated analyses in learning environments. Despite their instructional importance, run-ons are underrepresented in major grammatical error correction (GEC) benchmarks. We present a two-stage NLP pipeline for run-on detection and minimal-change correction, designed within the Learning Engineering Framework to improve writing feedback while preserving student voice. Early annotation of 251 student sentences identified 29 potential run-ons, informing our pipeline design and human validation workflows. [This paper was published in: "Proceedings of the Learning Engineering Research Network Convening (LERN 2026)," 2026, pp. 160-163.]
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      – Text: English
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      Pagination:
        PageCount: 5
    Subjects:
      – SubjectFull: Writing Evaluation
        Type: general
      – SubjectFull: Natural Language Processing
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Sentence Structure
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      – SubjectFull: Grammar
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      – SubjectFull: Error Correction
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      – SubjectFull: Automation
        Type: general
      – SubjectFull: Student Writing Models
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      – SubjectFull: Instructional Design
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      – TitleFull: Automated Run-On Sentence Detection and Correction for Educational Writing
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              Y: 2026
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