CPVis: Evidence-based Multimodal Learning Analytics for Evaluation in Collaborative Programming
🤖 AI Summary
To address the challenge of real-time assessment of group and individual performance in collaborative programming learning among non-CS-major beginners, this paper introduces CPVis, an interactive visual analytics system. CPVis pioneers a “flower-shaped encoding” to represent multidimensional collaborative performance—including code contribution, interaction frequency, and temporal coordination—and integrates a time-evolving visualization with temporal behavioral modeling driven by multimodal data (e.g., editing logs, chat records, version commits). It further incorporates an evidence-based learning analytics framework to support pedagogically grounded interpretation. A user study with 22 instructors demonstrates that CPVis significantly enhances teachers’ insight into collaborative patterns (+38%), assessment intuitiveness (+42%), and confidence in instructional intervention (+35%), all with statistical significance (p < 0.01) compared to baseline systems.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Evaluate collaborative programming performance
Monitor group and individual progress
Provide dynamic visual assessment tools
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multimodal data collection
Flower-based visual encoding
Time-based collaborative behavior analysis
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