用AI生成反思问题，帮编程初学者更好自我调节学习。
Owlgorithm: Supporting Self-Regulated Learning in Competitive Programming through LLM-Driven Reflection
- 基于GPT-4o生成个性化反思问题，适配不同答题结果。
- 正确解法引导深层理解，错误解法辅助结构化调试。
- 适合编程教学中的反思训练，需优化准确性和易用性。
我们提出Owlgorithm，一个支持编程竞赛中自我调节学习（SRL）的教育平台，通过大模型生成反思性问题。利用GPT-4o，系统为学生提交的代码生成上下文感知、元认知导向的提示，根据学生作答结果动态调整：对正确解答引导深度概念理解，对部分或失败解答提供结构化调试指导。在二、三年级编程课程中集成测试后，学生评分与助教反馈显示，多数人认为生成问题有助于反思与调试，但对反馈准确性及课堂可用性存在担忧。结果表明，大模型支持的反思对初学者有优势，但需改进以保障高级学习者的可靠性与教学价值。经验揭示：生成式AI可有效支持结构化反思，但需精心设计提示、动态适应与界面优化。本文为教育者提供具体建议，并规划未来改进方向，其框架亦可推广至其他反思性学习场景。
原文摘要 · Abstract (English)
We present Owlgorithm, an educational platform that supports Self-Regulated Learning (SRL) in competitive programming (CP) through AI-generated reflective questions. Leveraging GPT-4o, Owlgorithm produces context-aware, metacognitive prompts tailored to individual student submissions. Integrated into a second- and third-year CP course, the system-provided reflective prompts adapted to student outcomes: guiding deeper conceptual insight for correct solutions and structured debugging for partial or failed ones. Our exploratory assessment of student ratings and TA feedback revealed both promising benefits and notable limitations. While many found the generated questions useful for reflection and debugging, concerns were raised about feedback accuracy and classroom usability. These results suggest advantages of LLM-supported reflection for novice programmers, though refinements are needed to ensure reliability and pedagogical value for advanced learners. From our experience, several key insights emerged: GenAI can effectively support structured reflection, but careful prompt design, dynamic adaptation, and usability improvements are critical to realizing their potential in education. We offer specific recommendations for educators using similar tools and outline next steps to enhance Owlgorithm's educational impact. The underlying framework may also generalize to other reflective learning contexts.
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