用大模型自动分析编程过程数据，生成学习反馈。
On the Opportunities of Large Language Models for Programming Process Data
- 利用大模型将编程过程数据转换为可读摘要。
- 自动生成针对编程过程的形成性反馈。
- 适合教育研究者与自动化教学系统开发者。
计算教育工作者和研究人员长期使用编程过程数据来理解程序构建方式及学生遇到的困难。尽管这类数据在提供反馈方面具有潜力，但全自动的编程过程反馈系统仍处于探索阶段。大语言模型（LLMs）的兴起为多个领域研究者带来了新机遇。LLMs擅长跨格式内容转换，并能利用其训练所获知识进行推理。本文探讨了使用LLMs分析编程过程数据的潜力，并通过案例研究展示了如何利用LLMs自动总结编程过程并生成形成性反馈。整体研究表明，计算教育研究与实践社区正迈向自动化编程过程反馈的一步。
原文摘要 · Abstract (English)
Computing educators and researchers have used programming process data to understand how programs are constructed and what sorts of problems students struggle with. Although such data shows promise for using it for feedback, fully automated programming process feedback systems have still been an under-explored area. The recent emergence of large language models (LLMs) have yielded additional opportunities for researchers in a wide variety of fields. LLMs are efficient at transforming content from one format to another, leveraging the body of knowledge they have been trained with in the process. In this article, we discuss opportunities of using LLMs for analyzing programming process data. To complement our discussion, we outline a case study where we have leveraged LLMs for automatically summarizing the programming process and for creating formative feedback on the programming process. Overall, our discussion and findings highlight that the computing education research and practice community is again one step closer to automating formative programming process-focused feedback.
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