Developing a Practical Multi-Tool Pipeline for Analysing Synchronous Online Discussions
Abstract
In classroom-based and computer-mediated communication research, learner talk analysis has traditionally relied on manual transcription and coding, which are labor-intensive and difficult to scale. This methodological paper proposes a systematic, technology-assisted pipeline for analyzing students’ English use as a manifestation of willingness to communicate (WTC) during synchronous online breakout room discussions. Drawing on 15 sessions from a first-year English course, the study examined learner talk through multiple dimensions: turn frequency, lexical richness, speaking duration, and communicative functions, using Python, PRAAT, NVivo, and spreadsheet tools. A tailored Python script, LexiTurn, was developed to filter non-target elements and generate accurate token and type counts per turn. Outputs from all tools were integrated in Excel to produce a coherent, multidimensional dataset. The workflow demonstrates how targeted tool selection and basic coding skills, or collaboration with programmers, can enhance methodological rigor, efficiency, and replicability. Beyond its methodological contribution, the study offers pedagogical implications by illustrating how discourse analytics can help teachers identify participation gaps and promote equitable interaction. The proposed pipeline and LexiTurn will be made publicly available to support future classroom discourse research.
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