VizGroup: An AI-Assisted Event-Driven System for Real-Time Collaborative Programming Learning Analytics
Xiaohang Tang, Sam Wong, Kevin Pu, Xi Chen, Yalong Yang, Yan Chen
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.