  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.
