{
  "id": 18363,
  "url": "https://arxiv.org/abs/2608.10276",
  "title": "Fine-Tuning Large Language Models for Codebook-Guided Coding of Students' Mathematics Metaphor Responses",
  "summary": "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",
  "authors": "Liang Zhang, Stephen Hwang, Yue Ma, Jinfa Cai",
  "category": "research",
  "topics": "children-education",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T04:00:00.000Z",
  "fetched_at": "2026-08-12T05:10:43.828Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/18363",
  "original_url": "https://arxiv.org/abs/2608.10276",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}