VizGroup: An AI-assisted Event-driven System for Collaborative Programming Learning Analytics

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 in a comparison study using a dataset collected from a Peer Instruction activity that was conducted in a large programming lecture. The results showed that VizGroup helped instructors effectively overview, narrow down, and track nuances throughout students’ behaviors.

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Publication Details

Published
2024-10-11
DOI
https://doi.org/10.1145/3654777.3676347
Citations
11
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
3.82
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article

VizGroup: An AI-assisted Event-driven System for Collaborative Programming Learning Analytics

Xiaohang Tang, Yalong Yang, Kevin Pu, Yan Chen et al.
11 citations
Intelligent Tutoring Systems and Adaptive Learning
3.82
article

VizGroup: An AI-assisted Event-driven System for Collaborative Programming Learning Analytics

Xiaohang Tang, Yalong Yang, Kevin Pu, Yan Chen, Xi Chen, Sam Wong
article en
11 citations

Abstract

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 in a comparison study using a dataset collected from a Peer Instruction activity that was conducted in a large programming lecture. The results showed that VizGroup helped instructors effectively overview, narrow down, and track nuances throughout students’ behaviors.

Georgia Institute of Technology (US), University of Toronto (CA), University of Washington (US), Virginia Tech (US)
Openalex Percentile: Top 6%
Intelligent Tutoring Systems and Adaptive Learning
3.82
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