用AI分析代码与沟通数据，公平评估小组编程作业中每个人的贡献。
TRACE: AI-Assisted Assessment of Collaborative Projects in Computer Science Education
- 结合代码仓库与沟通记录，用AI量化每个人的实际贡献。
- 试点显示评分与教师判断高度一致，学生满意度提升。
- 适合大规模课程，减轻教师评阅负担，提升评估透明度。
协作式小组项目是计算机科学教育的重要组成部分，有助于培养团队合作、问题解决及行业相关技能。然而，在小组环境中评估个人贡献仍具挑战性。传统方法如均分或主观同伴评价，往往缺乏公平性、客观性和可扩展性，尤其在大班教学中更为明显。我们提出TRACE，一种半自动化的AI辅助框架，通过代码仓库挖掘、沟通数据分析和AI辅助分析，同时评估项目质量和个体贡献。在一门软件工程课程中的试点部署表明，该方法与教师评分高度一致，提升了学生满意度，并显著降低了教师的评分工作量。结果表明，AI辅助分析可有效提升计算机科学教育中协作项目评估的透明度与可扩展性。
原文摘要 · Abstract (English)
Collaborative group projects are integral to computer science education, fostering teamwork, problem-solving, and industry-relevant skills. However, assessing individual contributions within group settings remains challenging. Traditional approaches, including equal grade distribution and subjective peer evaluations, often lack fairness, objectivity, and scalability, particularly in large classrooms. We propose TRACE, a semi-automated AI-assisted framework for assessing collaborative software projects that evaluates both project quality and individual contributions using repository mining, communication analytics, and AI-assisted analytics. A pilot deployment in a software engineering course demonstrated high alignment with instructor assessments, increased student satisfaction, and reduced instructor grading effort. The results suggest that AI-assisted analytics can improve the transparency and scalability of collaborative project assessment in computer science education.
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