用同伴互评提升编程课学习质量，应对AI辅助带来的评估挑战
From Coders to Critics: Empowering Students through Peer Assessment in the Age of AI Copilots
- 设计匿名化评分表，让学生互评2D游戏项目
- 同伴评分与教师评分相关性中等，误差在可接受范围
- 学生普遍认可公平性，更愿给出建设性反馈
AI编程助手（如ChatGPT）的普及正在重塑编程教育，引发对评估方式、学术诚信和能力培养的担忧。本文在大型入门编程课程中开展实证研究，采用基于评分标准的匿名同伴评审机制，让学生互评最终项目（2D游戏）。通过相关性、平均绝对误差和均方根误差（RMSE）对比同伴评分与教师评分。此外，47个小组的反思问卷揭示了学生对公平性、评分行为及成绩汇总方式的看法。结果表明，同伴互评可实现中等精度的教师评价替代，促进学生参与度、评价思维以及提供高质量反馈的兴趣。研究为构建可扩展、可信的同伴评估系统以应对AI辅助编程时代提供了依据。
原文摘要 · Abstract (English)
The rapid adoption of AI powered coding assistants like ChatGPT and other coding copilots is transforming programming education, raising questions about assessment practices, academic integrity, and skill development. As educators seek alternatives to traditional grading methods susceptible to AI enabled plagiarism, structured peer assessment could be a promising strategy. This paper presents an empirical study of a rubric based, anonymized peer review process implemented in a large introductory programming course. Students evaluated each other's final projects (2D game), and their assessments were compared to instructor grades using correlation, mean absolute error, and root mean square error (RMSE). Additionally, reflective surveys from 47 teams captured student perceptions of fairness, grading behavior, and preferences regarding grade aggregation. Results show that peer review can approximate instructor evaluation with moderate accuracy and foster student engagement, evaluative thinking, and interest in providing good feedback to their peers. We discuss these findings for designing scalable, trustworthy peer assessment systems to face the age of AI assisted coding.
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