AI 中文总结
研究针对基于项目式学习中传统评估难以捕捉高阶思维的问题，引入设计问题（DPs），通过调查教师看法、评估大语言模型生成的DPs及学生表现数据等，发现DPs是传统评估的有益补充，能捕捉高阶思维不同方面。
AI 中文摘要
基于项目的学习（PjBL）在计算机教育中很常见，但传统评估往往无法捕捉高阶思维（HOT），尤其是在迁移情境中。本研究引入“设计问题”（DPs）来解决这一差距，它是基于场景的简洁提示，要求在新情境中应用项目概念。研究调查了教师看法、大语言模型生成DPs的能力及学生体验。对31名教师的调查、80个大语言模型生成的DPs评估和学生表现数据表明，教师重视DPs，但创作难度是障碍。大语言模型生成的高质量提示得到专家高度认可。学生对不同大语言模型生成的DPs评价相似，其在DP任务上的表现与传统项目成绩相关性可忽略不计，这表明DPs可能捕捉到HOT的不同方面。击键数据也表明学生通过规划和修订行为有更深层次的认知参与。总体而言，DPs似乎是传统评估的有用补充，特别是在人工智能使用或协作可能损害个体学习的情况下。
英文摘要
Project-based learning (PjBL) is common in computing education, but traditional assessments of PjBL often fail to capture higher-order thinking (HOT), especially in transfer contexts. This study introduces "design problems" (DPs): concise, scenario-based prompts that require applying project concepts in new situations, to address this gap. We examined instructor perceptions, the ability of large language models (LLMs) to generate DPs, and student experiences. Surveys of 31 instructors, evaluation of 80 LLM-generated DPs, and student performance data showed that while instructors value DPs, creation effort is a barrier. LLMs helped by producing high-quality prompts with strong expert agreement. Students rated DPs from different LLMs similarly, and their performance on DP tasks showed negligible correlation with traditional project grades, suggesting DPs may capture distinct aspects of HOT. Keystroke data also suggested deeper cognitive engagement of students through planning and revision behaviors. Overall, DPs appear to be a useful complement to traditional assessments, especially in situations where AI use or collaboration may undermine individual learning.
CommentsAccepted to appear in Proceedings of the 2nd ACM Virtual Global Computing Education Conference V.1 (SIGCSE Virtual 2026). DOI: 10.1145/3795867.3831014
