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Comparing Native and Non-native English Speakers' Behaviors in Collaborative Writing through Visual Analytics

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Chen, Yuexi, Xiao, Yimin, Zinat, Kazi Tasnim, Yamashita, Naomi, Gao, Ge, Liu, Zhicheng·2025·Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3706598.3713693
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Abstract

Understanding collaborative writing dynamics between native speakers (NS) and non-native speakers (NNS) is critical for enhancing collaboration quality and team inclusivity. In this paper, we partnered with communication researchers to develop visual analytics solutions for comparing NS and NNS behaviors in 162 writing sessions across 27 teams. The primary challenges in analyzing writing behaviors are data complexity and the uncertainties introduced by automated methods. In response, we present \textsc{COALA}, a novel visual analytics tool that improves model interpretability by displaying uncertainties in author clusters, generating behavior summaries using large language models, and visualizing writing-related actions at multiple granularities. We validated the effectiveness of \textsc{COALA} through user studies with domain experts (N=2+2) and researchers with relevant experience (N=8). We present the insights discovered by participants using \textsc{COALA}, suggest features for future AI-assisted collaborative writing tools, and discuss the broader implications for analyzing collaborative processes beyond writing.

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  • The selection process resulted in 16 interviews, a sample size consistent with prior exploratory research with hard-to-reach expert populations.

    “The selection process resulted in 16 interviews, a sample size consistent with prior exploratory research with hard-to-reach expert populations [see e.g. 4, 32, 154] (§7.5).”

    From “It’s Just a Wild, Wild West”: Harnessing Public Procurement as an AI Governance Mechanism (2026) · cites this paper as [32]

    Section 7.1 (p. 9) describes a small qualitative evaluation involving four communication researchers and eight graduate students, so the paper offers a precedent for small-scale exploratory research. It does not report 16 interviews or characterize its participants as a hard-to-reach expert population; §7.1 describes brief interviews only after the eight individual study sessions. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

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Sorted by citations

Authors
Chen, Y., Xiao, Y., Zinat, K. T., Yamashita, N., Gao, G., & Liu, Z.
Affiliations
  • University of Maryland, College Park (US)
  • NTT (Japan) (JP)
ORCID
Venue
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2025-04-25
Pages
1-16
DOI
10.1145/3706598.3713693
OpenAlex
W4409749132
Cited by
2
References
6
Citations by year
2026: 1 · 2025: 1
FWCI
1.51
Type
conference paper
Language
en
Open access
Licence
Locations
  • CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
NSF (National Science Foundation) (IIS#2239130, IIS#1947929)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
Keywords
collaborative writing, visual analytics, COALA, author clustering, uncertainty visualization, large language models, AI writing tools, model interpretability

About these checks

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.

Statistics and arithmetic are recomputed, not judged.

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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