| Sumario: | Background: Analysing classroom dialogue is a widely used approach for understanding students' learning, often requiring team‐based collaborative research. This presents a challenge for single researchers due to the labour‐intensive nature of the process. Emerging advancements in large language models (LLMs) such as ChatGPT, enhance qualitative research, particularly in inductive and deductive coding tasks. Objectives: This study investigates the feasibility of a single researcher, the author of this study, collaborating with ChatGPT‐4o for qualitative coding of classroom dialogue data. The goal is to develop effective human–ChatGPT co‐coding methods and explore how such collaboration can enhance qualitative coding practices and provide insights into students' dialogue patterns. Methods: The study analysed 1287 utterances from middle school science classes using a mixed‐method approach. A new codebook was developed through an inductive process using ChatGPT, followed by deductive coding conducted by both the researcher and ChatGPT. Kappa values were compared between human–human and human–ChatGPT coding. Disagreements in code assignments were resolved by the researcher, with reference to ChatGPT's rationale. Coded utterances were analysed using ordered network analysis (ONA) to visualise dialogue patterns in classes. Results and Conclusions: The coding conducted by the researcher and ChatGPT resulted in a Cohen's kappa of 0.56, with the highest level of disagreement observed in the category of Meta‐cognition. The inductively co‐developed codebook helped uncover students dialogue patterns during experimental activities. Although ChatGPT exhibited limitations in interpreting nuanced and context‐dependent utterances, the findings highlight its potential as a valuable collaborator for solo researchers by supporting cognitive processes such as reflective interpretation and the development of new perspectives.
|
|---|