{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T21:34:58Z","timestamp":1777152898322,"version":"3.51.4"},"reference-count":53,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computer Assisted Learning"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Analysing classroom dialogue is a widely used approach for understanding students' learning, often requiring team\u2010based collaborative research. This presents a challenge for single researchers due to the labour\u2010intensive nature of the process. Emerging advancements in large language models (LLMs) such as ChatGPT, enhance qualitative research, particularly in inductive and deductive coding tasks.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objectives<\/jats:title><jats:p>This study investigates the feasibility of a single researcher, the author of this study, collaborating with ChatGPT\u20104o for qualitative coding of classroom dialogue data. The goal is to develop effective human\u2013ChatGPT co\u2010coding methods and explore how such collaboration can enhance qualitative coding practices and provide insights into students' dialogue patterns.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The study analysed 1287 utterances from middle school science classes using a mixed\u2010method 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\u2013human and human\u2013ChatGPT 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results and Conclusions<\/jats:title><jats:p>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\u2010cognition. The inductively co\u2010developed codebook helped uncover students dialogue patterns during experimental activities. Although ChatGPT exhibited limitations in interpreting nuanced and context\u2010dependent 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.<\/jats:p><\/jats:sec>","DOI":"10.1111\/jcal.70089","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T01:30:10Z","timestamp":1751506210000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Co\u2010Coding Classroom Dialogue: A Single Researcher Case Study of <scp>ChatGPT<\/scp>\u2010Assisted Analysis in Science Education"],"prefix":"10.1111","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4373-6930","authenticated-orcid":false,"given":"Eunhye","family":"Shin","sequence":"first","affiliation":[{"name":"Hazard Literacy Center Ewha Womans University  Seoul South Korea"}]}],"member":"311","published-online":{"date-parts":[[2025,7,2]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2018.05.016"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/00461520.2015.1004069"},{"key":"e_1_2_10_4_1","unstructured":"Barany A. N.Nasiar C.Porter et\u00a0al.2024.\u201cChatGPT for Education Research: Exploring the Potential of Large Language Models for Qualitative Codebook Development.\u201dIn International Conference.https:\/\/doi.org\/10.1007\/978\u20103\u2010031\u201064299\u20109_10."},{"key":"e_1_2_10_5_1","first-page":"1877","article-title":"Language Models Are Few\u2010Shot Learners","volume":"33","author":"Brown T.","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cptl.2018.03.019"},{"key":"e_1_2_10_7_1","unstructured":"Chew R. 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