Use of Large Language Models for Extracting Knowledge Components in CS1 Programming Exercises
Use of Large Language Models for Extracting Knowledge Components in CS1 Programming Exercises
复制标题
使用大型语言模型提取 CS1 编程练习中的知识组件
DOI：
10.1145/3626253.3635592
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Norouzi, Narges
中科院分区：
文献类型：
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作者：
Niousha, Rose;Hoq, Muntasir;Akram, Bita;Norouzi, Narges
本研究利用大型语言模型提取基本的编程概念在CS1课程的编程作业。我们试图回答以下研究问题：RQ1。大型语言模型如何有效地从编程作业中识别CS1课程中的知识组件？RQ 2.大型语言模型是否可以用来提取程序级的知识组件，以及如何使用这些信息来识别学生的误解？初步结果表明，从一个大型的语言模型和专家生成的列表检索课程级的知识组件之间的高度相似性。
This study utilizes large language models to extract foundational programming concepts in programming assignments in a CS1 course. We seek to answer the following research questions: RQ1. How effectively can large language models identify knowledge components in a CS1 course from programming assignments? RQ2. Can large language models be used to extract program-level knowledge components, and how can the information be used to identify students' misconceptions? Preliminary results demonstrated a high similarity between course-level knowledge components retrieved from a large language model and that of an expert-generated list.