VizGroup: An AI-Assisted Event-Driven System for Real-Time Collaborative Programming Learning Analytics
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913535185911808 |
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| author | Tang, Xiaohang Wong, Sam Pu, Kevin Chen, Xi Yang, Yalong Chen, Yan |
| author_facet | Tang, Xiaohang Wong, Sam Pu, Kevin Chen, Xi Yang, Yalong Chen, Yan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08743 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VizGroup: An AI-Assisted Event-Driven System for Real-Time Collaborative Programming Learning Analytics Tang, Xiaohang Wong, Sam Pu, Kevin Chen, Xi Yang, Yalong Chen, Yan Human-Computer Interaction 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. |
| title | VizGroup: An AI-Assisted Event-Driven System for Real-Time Collaborative Programming Learning Analytics |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2404.08743 |