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Drill-type adaptive debugging to boost programming skills
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AI
DOI：10.1080/08993408.2026.2672717.png)
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Abstract
En 中文
Informatics Education research has often called for including the coverage of debugging skills in education, whose acquisition also fosters the learning of programming. Programming and debugging alike are complex tasks, whose mastering requires operating on multiple interconnected parts simultaneously. Cognitive Load Theory suggests that reducing the complexity of how new information is presented improves educational outcomes. Complex concepts may be better understood when encountered in isolated contexts first, before being combined into complex scenarios. Focusing on isolated elements is highlighted as a research gap by a recent review of the literature on programming education.
To fill this research gap by designing a programming-oriented intervention focused on isolated elements. The intervention is based on drill-type exercises, delivered through an adaptive system, which focus on debugging and further scaffold an established educational approach based on Code.org, in the intent of reducing its cognitive load.
A pre-test/post-test study with 89 fourth-grade learners, comparing the impact on programming skills of our experimental intervention against approaches based on Code.org and standard STEM activities.
Our results show a significant difference in post-test performance across the experimental conditions, with the experimental adaptive drill-type intervention leading to better overall performance.
This work showcases the potential of our approach as an effective educational strategy. We also highlight the benefits that focus on isolated elements and adaptive drill-type exercises may have even on environments specifically designed for novices, such as Code.org.
Keywords:
Block-based programming languages
adaptive learning
debugging
Cognitive Load Theory