CoRemix: Supporting Informal Learning in Scratch Community With Visual Graph and Generative AI
December 07, 2024 Β· Declared Dead Β· π arXiv.org
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Authors
Yunnong Chen, Yishu Shen, Ruiyi Liu, Xinyu Yu, Lingyun Sun, Liuqing Chen
arXiv ID
2412.05559
Category
cs.HC: Human-Computer Interaction
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Online programming communities provide a space for novices to engage with computing concepts, allowing them to learn and develop computing skills using user-generated projects. However, the lack of structured guidance in the informal learning environment often makes it difficult for novices to experience progressively challenging learning opportunities. Learners frequently struggle with understanding key project events and relations, grasping computing concepts, and remixing practices. This study introduces CoRemix, a generative AI-powered learning system that provides a visual graph to present key events and relations for project understanding. We propose a visual-textual scaffolding to help learners construct the visual graph and support remixing practice. Our user study demonstrates that CoRemix, compared to the baseline, effectively helps learners break down complex projects, enhances computing concept learning, and improves their experience with community resources for learning and remixing.
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