用编程课问答数据微调大模型，提升其教学契合度。
Towards Pedagogical LLMs with Supervised Fine Tuning for Computing Education
- 基于2500条编程课论坛问答对进行有监督微调。
- 微调后模型更符合建构主义等教育原则。
- 适合教育科技研究者与智能助教开发者参考。
本文研究通过有监督微调提升大语言模型在计算教育中的教学契合度，解决大模型可能影响学习效果的担忧。项目采用一个包含2500个高质量编程课程论坛问答对的专有数据集，探讨两个问题：大学课程论坛是否适合作为微调数据来源，以及有监督微调如何改善大模型与建构主义等教育原则的对齐。初步结果表明，大模型的教学契合度有所提升，但需进一步深入评估。
原文摘要 · Abstract (English)
This paper investigates supervised fine-tuning of large language models (LLMs) to improve their pedagogical alignment in computing education, addressing concerns that LLMs may hinder learning outcomes. The project utilised a proprietary dataset of 2,500 high quality question/answer pairs from programming course forums, and explores two research questions: the suitability of university course forums in contributing to fine-tuning datasets, and how supervised fine-tuning can improve LLMs' alignment with educational principles such as constructivism. Initial findings suggest benefits in pedagogical alignment of LLMs, with deeper evaluations required.
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