研究学生如何使用AI生成的编程提示，发现应对无用提示的实用策略。
Understanding Student Interaction with AI-Powered Next-Step Hints: Strategies and Challenges
- 通过过程挖掘分析34名学生在IDE中使用提示的行为模式。
- 识别出16种常见交互场景，揭示学生如何调整代码以获取新提示。
- 适合教育AI、编程学习系统设计者参考，提升提示有效性。
自动化反馈生成在计算机科学教育中对个性化学习至关重要。其中，下一步提示反馈尤为关键，能为学生提供解决编程任务的具体行动步骤。本研究探讨了学生在集成开发环境（IDE）中与AI驱动的下一步提示系统互动的方式。我们收集并分析了34名学生完成Kotlin编程任务时的详细提示交互日志，采用过程挖掘技术识别出16种常见交互模式。对6名学生的半结构化访谈显示，面对无用提示时，学生常通过部分采纳提示或修改代码来生成新的提示变体。这些发现结合公开发布的数据集，为未来研究提供了宝贵资源，并深入揭示了学生行为特征，有助于优化提示设计以增强学习支持。
原文摘要 · Abstract (English)
Automated feedback generation plays a crucial role in enhancing personalized learning experiences in computer science education. Among different types of feedback, next-step hint feedback is particularly important, as it provides students with actionable steps to progress towards solving programming tasks. This study investigates how students interact with an AI-driven next-step hint system in an in-IDE learning environment. We gathered and analyzed a dataset from 34 students solving Kotlin tasks, containing detailed hint interaction logs. We applied process mining techniques and identified 16 common interaction scenarios. Semi-structured interviews with 6 students revealed strategies for managing unhelpful hints, such as adapting partial hints or modifying code to generate variations of the same hint. These findings, combined with our publicly available dataset, offer valuable opportunities for future research and provide key insights into student behavior, helping improve hint design for enhanced learning support.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。