As programming education becomes more widespread, many college students from non-computer science backgrounds begin learning programming. Collaborative programming emerges as an effective method for instructors to support novice students in developing coding and teamwork abilities. However, due to limited class time and attention, instructors face challenges in monitoring and evaluating the progress and performance of groups or individuals. To address this issue, we collect multimodal data from real-world settings and develop CPVis, an interactive visual analytics system designed to assess student collaboration dynamically. Specifically, CPVis enables instructors to evaluate both group and individual performance efficiently. CPVis employs a novel flower-based visual encoding to represent performance and provides time-based views to capture the evolution of collaborative behaviors. A within-subject experiment (N=22), comparing CPVis with two baseline systems, reveals that users gain more insights, find the visualization more intuitive, and report increased confidence in their assessments of collaboration.
@article{arxiv.2502.17835,
title = {CPVis: Evidence-based Multimodal Learning Analytics for Evaluation in Collaborative Programming},
author = {Gefei Zhang and Shenming Ji and Yicao Li and Jingwei Tang and Jihong Ding and Meng Xia and Guodao Sun and Ronghua Liang},
journal= {arXiv preprint arXiv:2502.17835},
year = {2025}
}