Classroom Discourse

Classroom discourse analysis aims to understand how verbal interactions in educational settings influence learning and teaching effectiveness. Current research heavily utilizes natural language processing (NLP) techniques, particularly large language models (LLMs) like GPT-4 and RoBERTa, to automate the analysis of transcribed classroom dialogues, improving efficiency and scalability compared to traditional manual coding. This automated analysis allows researchers to identify patterns in teacher-student interactions, such as questioning styles (e.g., funneling vs. focusing) and the overall quality of discussions, ultimately informing the development of improved teaching practices and assessment tools.