AI生成动画助学编程，效果因人而异。
AI-Generated Traces for Novice Programmers: Learning Effects and Learner Differences in a Multi-Institutional Study
- 用AI生成带比喻的动画演示代码执行过程。
- 即时学习有提升，但长期效果不显著。
- 活跃学生受益更大，需个性化教学支持。
入门级编程课程（CS1）常难以帮助学生理解程序运行过程。尽管可视化能显式呈现执行流程，其效果依赖于设计与情境，而基于AI的可视化实证研究仍有限。本文提出生成式动画追踪（GATs），一种结合源代码、执行状态与概念类比的AI生成、带解说的动画。在两所高校的Python（N=961）和Java（N=151）课程中，对比GATs与文本解释对学习效果的影响。测量了即时学习表现、学习体验、期末参与度及考试成绩。结果显示，GATs在即时学习上具有选择性优势，但效果受上下文影响且为短期。学习者参与度差异调节了GATs对成绩的影响，凸显个性化教学的重要性。
原文摘要 · Abstract (English)
Introductory programming (CS1) courses often struggle to support students' understanding of program execution. While visualizations can make execution processes explicit, their effectiveness depends on design and context, and empirical evidence for AI-generated visualizations remains limited. We propose Generated Animated Traces (GATs), AI-generated, analogy-based, narrated animations that coordinate source code, execution state, and conceptual analogies. We conduct a study at two institutions in CS1 courses (Python, N=961; Java N=151) comparing GATs to textual explanations. We measure immediate learning performance and experience, end-of-course engagement and exam performance. Results show that GATs can yield selective benefits for immediate learning, but benefits are context-dependent and short-term. We observe that GATs' influence on performance is moderated by learner engagement profiles. This finding underscores the importance of personalized approaches.
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