Programming instructors often conduct collaborative learning activities, like Peer Instruction, to foster a deeper understanding in students and enhance their engagement with learning. These activities, however, may not always yield productive outcomes due to the diversity of student mental models and their ineffective collaboration. In this work, we introduce VizGroup, an AI-assisted system that enables programming instructors to easily oversee students' real-time collaborative learning behaviors during large programming courses. VizGroup leverages Large Language Models (LLMs) to recommend event specifications for instructors so that they can simultaneously track and receive alerts about key correlation patterns between various collaboration metrics and ongoing coding tasks. We evaluated VizGroup with 12 instructors in a comparison study using a dataset collected from a Peer Instruction activity that was conducted in a large programming lecture. The results showed that VizGroup helped instructors effectively overview, narrow down, and track nuances throughout students' behaviors.

翻译：编程教师常开展协作学习活动（如同伴教学法）以深化学生理解并提升其学习参与度。然而，由于学生心智模型的多样性及协作效率不足，此类活动未必总能产生积极成效。本研究提出VizGroup——一种AI辅助系统，可帮助编程教师在大规模编程课程中便捷监控学生实时协作学习行为。该系统利用大语言模型为教师推荐事件规则，使其能够同步追踪并接收关于多维度协作指标与实时编程任务间关键关联模式的预警。我们通过对比研究，使用从大型编程讲座中同伴教学活动收集的数据集，对12名教师进行了VizGroup系统评估。结果表明，VizGroup能有效协助教师全面概览、精准定位并持续追踪学生学习行为中的细微特征。