LLM-assisted sentiment analysis for integrated computational and qualitative mixed methods education research: A case study of students' written reflection assignments
arXiv:2605.27403v1 Announce Type: cross Abstract: Written reflection assignments give students valuable opportunities for critical self-assessment, meaning making, and learning processing. Additionally, such reflections provide rich data for qualitative education research. However, qualitative data can be time-consuming to analyze. It is even more time-intensive to qualitatively compare findings between different groups of participants, usually limiting comparison to, at most, one variable (e.g., binary gender). Large language models (LLMs) have recently begun to be critically evaluated for us
The proliferation of powerful LLMs is prompting researchers to explore their practical applications in traditionally labor-intensive analytical fields, like qualitative research, right now.
This development indicates a tangible shift towards AI supporting and augmenting complex human analysis, potentially accelerating research timelines and broadening the scope of qualitative studies.
The speed and scale at which qualitative data, particularly written reflections, can be analyzed and compared is now significantly enhanced, potentially enabling more robust and data-rich educational research.
- · Education researchers
- · Qualitative research methodologies
- · LLM developers
- · Traditional manual qualitative analysis
Researchers gain new tools to process large volumes of qualitative data more efficiently.
The ability to conduct larger-scale comparative qualitative studies could lead to new insights and more generalized findings in education.
This could democratize access to advanced analytical capabilities for researchers without extensive qualitative analysis teams, enhancing research output across institutions.
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