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 using a dataset collected from a Peer Instruction activity that was conducted in a large programming lecture. The results showed that compared to a version of VizGroup without the suggested units, VizGroup with suggested units helped instructors create additional monitoring units on previously undetected patterns on their own, covered a more diverse range of metrics, and influenced the participants' following notification creation strategies.

翻译：摘要：编程教师常采用同行教学（Peer Instruction）等协作学习活动，以促进学生深度理解并增强学习参与度。然而，由于学生心智模型的多样性和协作效率的差异，这些活动未必总能产生富有成效的学习成果。本研究提出VizGroup系统，这是一套人工智能辅助系统，使编程教师能够在大规模编程课程中轻松监控学生的实时协作学习行为。VizGroup利用大语言模型（LLMs）为教师推荐事件规范，使其能同步追踪并接收关于各类协作指标与进行中编码任务之间关键关联模式的警报。我们通过从大型编程课程开展的同行教学活动中采集的数据集，对12名教师进行了VizGroup评估。结果表明，相较于不含建议单元版本的VizGroup，含建议单元的VizGroup不仅帮助教师针对先前未检测到的模式自主创建了更多监控单元，还覆盖了更多元化的指标维度，并影响了参与者后续的通知创建策略。