Comparing Native and Non-native English Speakers' Behaviors in Collaborative Writing through Visual Analytics
This work addresses the need for better collaboration quality and inclusivity in team settings, though it is incremental as it builds on existing visual analytics methods.
The paper tackled the problem of understanding collaborative writing dynamics between native and non-native English speakers by developing COALA, a visual analytics tool that improved model interpretability and generated behavior summaries, validated through user studies with 12 participants.
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.