Fine-Tuning Large Language Models for Codebook-Guided Coding of Students' Mathematics Metaphor Responses
arXiv:2608.10276v1 Announce Type: cross Abstract: Student-generated metaphors about mathematics can reveal students' attitudes, beliefs, identities, and experiences, but human expert coding of these thematically and semantically complex open-ended responses is time-intensive and difficult to scale. This study examines whether LoRA-based supervised fine-tuning of large language models (LLMs) can improve their performance on codebook-guided coding tasks for student mathematics metaphors. We used a
Record details
Published: 12 August 2026
Source: arXiv cs.CY
Category: Research
Topics: Children & education
Retrieved: 12 August 2026
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ethics.ai (12 August 2026), “Fine-Tuning Large Language Models for Codebook-Guided Coding of Students' Mathematics Metaphor Responses,” evidence record 18363, https://ethics.ai/record/18363 (originally published by arXiv cs.CY).
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