用大模型自动发现学生编程中的理解误区。
McMining: Automated Discovery of Misconceptions in Student Code
- 基于大模型分析学生代码，识别隐藏的编程误解
- 在多个大模型中验证，发现准确率超80%的误解
- 适合教育科技、编程教学系统开发者使用
学生学习编程时常产生对语言概念的误解，这些误解不仅导致错误或低效代码，还会阻碍后续知识掌握。本文提出McMining任务——从学生代码样本中自动挖掘编程误解。为支持该任务的训练与评估，我们构建了一个可扩展的误解基准数据集，并包含大量体现这些误解的代码样本。随后，我们设计了两种基于大模型的McMiner方法，通过大量实验表明，Gemini、Claude和GPT系列模型在识别学生代码中的误解方面表现良好。
原文摘要 · Abstract (English)
When learning to code, students often develop misconceptions about various programming language concepts. These can not only lead to bugs or inefficient code, but also slow down the learning of related concepts. In this paper, we introduce McMining, the task of mining programming misconceptions from samples of code from a student. To enable the training and evaluation of McMining systems, we develop an extensible benchmark dataset of misconceptions together with a large set of code samples where these misconceptions are manifested. We then introduce two LLM-based McMiner approaches and through extensive evaluations show that models from the Gemini, Claude, and GPT families are effective at discovering misconceptions in student code.
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