Sentiment analysis of preservice teachers' reflections using a large language model

TL;DR

This paper explores the use of large language models like GPT-4, Gemini, and BERT to analyze the sentiment of preservice teachers' reflections, comparing their effectiveness and highlighting the need for tailored analysis methods.

Contribution

It introduces a comparative analysis of LLMs for sentiment analysis in teacher reflections and emphasizes developing relevant analysis frameworks for educational use.

Findings

LLMs can categorize reflections with varying accuracy.

Developing tailored analysis methods improves relevance for teacher education.

Comparison reveals strengths and limitations of each LLM.

Abstract

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.