Sentiment analysis of preservice teachers' reflections using a large language model
This work addresses the need for better integration of large language models into teacher education to support preservice teachers and educators, though it is incremental as it focuses on comparing existing tools without introducing new methods.
The study analyzed preservice teachers' reflections using sentiment analysis with GPT-4, Gemini, and BERT to compare how these tools categorize emotions and tones, aiming to bridge gaps between qualitative, quantitative, and computational analyses in teacher education.
In this study, the emotion and tone of preservice teachers' reflections were analyzed using sentiment analysis with LLMs: GPT-4, Gemini, and BERT. We compared the results to understand how each tool categorizes and describes individual reflections and multiple reflections as a whole. This study aims to explore ways to bridge the gaps between qualitative, quantitative, and computational analyses of reflective practices in teacher education. This study finds that to effectively integrate LLM analysis into teacher education, developing an analysis method and result format that are both comprehensive and relevant for preservice teachers and teacher educators is crucial.