CoRemix: Supporting Informal Learning in Scratch Community With Visual Graph and Generative AI

TL;DR

CoRemix is an AI-powered system that uses visual graphs and scaffolding to improve novices' understanding and remixing skills in Scratch programming communities.

Contribution

The paper introduces CoRemix, a novel AI-driven tool that visualizes project events and relations to support informal learning in Scratch communities.

Findings

CoRemix helps learners understand complex projects better.

It enhances learners' grasp of computing concepts.

It improves user experience in remixing and community engagement.

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.