AI 中文总结
针对编程教育中学习者异质性难题，介绍CodeOwl这一人工智能驱动工具自动生成分层帕森斯问题，经混合方法框架评估，该工具生成的问题难度提升效果好，获专家、教师和学生认可，不过教师和学生也分别提出了改进意见。
AI 中文摘要
在编程教育中应对学习者的异质性具有挑战性，因为学生的速度、先验知识和动机存在差异。差异化教学，如分层序列，能让学生在适当难度水平参与学习，但手动创建这些资源劳动强度大。本文介绍了CodeOwl，这是一个由人工智能驱动的工具，可自动生成分层帕森斯问题。从示例任务或特定编程概念出发，它能自动生成分层序列。通过混合方法框架评估发现，98.7%的序列难度从第1层成功提升到第３层，专家认为问题陈述清晰，教师肯定其效用但希望更好地与课程对齐，学生反馈积极但要求增强反馈机制和交互模式。
英文摘要
Addressing learner heterogeneity in programming education is challenging due to variations in student speed, prior knowledge, and motivation. While differentiated instruction, such as tiered sequences, allows students to engage at appropriate difficulty levels, manually creating these resources is labour-intensive. This paper introduces CodeOwl, an AI-driven tool that automates the generation of tiered Parsons problems. Starting from a sample task or specific programming concepts, CodeOwl produces tiered sequences of Parsons problems automatically. We evaluated CodeOwl with a mixed-method framework comprising complexity analysis, expert ratings, and user studies. Analysis of 297 tiered sequences (three tiers each) revealed that 98.7% achieved a positive complexity increase, successfully rising in difficulty from Tier 1 to Tier 3. Experts rated the generated problem statements as highly clear. While teachers praised the tool's utility, they identified a need for greater control over curriculum alignment. Similarly, students reported positively but requested enhanced feedback mechanisms and alternative interaction modes.
CommentsTo appear at the 38th CSEE&T
