AI 中文总结
该报告介绍两个大语言模型驱动的支架系统，用于支持不同计算场景的问题提出。系统用布鲁姆分类法评估问题，输出方式有间接（引导性问题）和直接（示例）两种。研究表明二者互补，先间接后直接能促进反思与改进，为计算课堂整合此类支架提供策略。
AI 中文摘要
在计算教育中，问题提出是一项有价值的学习活动，鼓励学习者积极构建、完善和反思问题，而不仅仅是解决问题。本经验报告介绍了两个由大语言模型驱动的支架系统的设计和试点部署，用于在两个具有不同任务开放性水平的计算场景中支持问题提出。两个系统都使用基于布鲁姆分类法的标准评估学生提出的问题，并应用相同的评估框架，仅在输出方式上有所不同：一个提供引导性问题（间接支架），另一个提供示例（直接支架）。我们对20名研究生进行了一项被试内、平衡的试点研究，收集了问题质量评级、用户体验调查和课后访谈。我们的部署表明，两个系统以互补的方式支持问题完善，各有不同益处。直接支架能带来更大的即时改进，而访谈显示参与者重视间接支架促进对自身问题设计的深入反思。基于这些发现，我们建议先采用间接支架促进反思，当学习者遇到困难时再转向直接支架。这些经验为将基于大语言模型的支架整合到计算课堂提供了初步实用策略。
英文摘要
Problem posing is a valuable learning activity in computing education, encouraging learners to actively construct, refine, and reflect on problems rather than simply solving them. This experience report presents the design and pilot deployment of two LLM-powered scaffolding systems for supporting problem posing across two computational scenarios with different levels of task openness. Both systems assessed student-generated problems using Bloom's Taxonomy-based criteria and applied the same assessment framework, differing only in output modality: one provided guiding questions (Indirect scaffolding), while the other offered worked examples (Direct scaffolding). We conducted a within-subjects, counterbalanced pilot study with 20 graduate students and collected problem-quality ratings, user-experience surveys, and post-session interviews. Our deployment showed that both systems supported problem refinement in complementary ways, each offering distinct benefits. Direct scaffolding produced greater immediate improvements, while interviews showed that participants valued Indirect scaffolding for promoting deeper reflection on their own problem design. Based on these findings, we suggest sequencing the two modalities by beginning with Indirect scaffolding to promote reflection, then shifting to Direct scaffolding when learners become stuck. These lessons offer an initial practical strategy for integrating LLM-based scaffolding into computing classrooms.
