DBox: Scaffolding Algorithmic Programming Learning through Learner-LLM Co-Decomposition
Journal:
arXiv
Published Date:
Feb 26, 2025
Abstract
Decomposition is a fundamental skill in algorithmic programming, requiring
learners to break down complex problems into smaller, manageable parts.
However, current self-study methods, such as browsing reference solutions or
using LLM assistants, often provide excessive or generic assistance that
misaligns with learners' decomposition strategies, hindering independent
problem-solving and critical thinking. To address this, we introduce
Decomposition Box (DBox), an interactive LLM-based system that scaffolds and
adapts to learners' personalized construction of a step tree through a
"learner-LLM co-decomposition" approach, providing tailored support at an
appropriate level. A within-subjects study (N=24) found that compared to the
baseline, DBox significantly improved learning gains, cognitive engagement, and
critical thinking. Learners also reported a stronger sense of achievement and
found the assistance appropriate and helpful for learning. Additionally, we
examined DBox's impact on cognitive load, identified usage patterns, and
analyzed learners' strategies for managing system errors. We conclude with
design implications for future AI-powered tools to better support algorithmic
programming education.
