“像套娃一样”：分析计算机科学专业学生使用大语言模型生成的递归类比
"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models
AI总结：
本研究聚焦计算机教学中递归概念的学习难点，探究ChatGPT辅助学生生成个性化递归类比的效果，验证了学生结合个人兴趣生成的类比多样性更强、更易记忆，且能提升学习体验与理解程度。
摘要：
掌握复杂的计算机概念对学生而言往往是一项挑战，他们难以将这些新知识点与熟悉的经验和认知锚定关联。为解决这一问题，优质的类比能在陌生概念与熟悉概念之间搭建桥梁，以颇具吸引力的方式辅助理解。但即便对经验丰富的教师来说，创作有效的教学类比喻也并非易事。我们探究了大语言模型（LLM），具体为ChatGPT，能在多大程度上按需生成与个人相关的类比。我们聚焦递归这一具有难度的门槛概念，开展了一项调查，分析了350余名计算机专业一年级学生生成的类比。研究人员向学生提供一段代码片段，要求他们借助ChatGPT生成自己的递归类类比，还可选择在提示词中加入个人感兴趣的主题。我们观察到，加入学生指定主题后生成的类比呈现出极高的多样性，这与未加入时的通用类比喻形成鲜明对比，凸显了学生与大语言模型协作时创造力的价值。学生不仅喜爱这项活动，还表示对递归的理解有所提升，同时他们称那些与个人及文化相关的类比更易记忆。
英文摘要：
Grasping complex computing concepts often poses a challenge for students who struggle to anchor these new ideas to familiar experiences and understandings. To help with this, a good analogy can bridge the gap between unfamiliar concepts and familiar ones, providing an engaging way to aid understanding. However, creating effective educational analogies is difficult even for experienced instructors. We investigate to what extent large language models (LLMs), specifically ChatGPT, can provide access to personally relevant analogies on demand. Focusing on recursion, a challenging threshold concept, we conducted an investigation analyzing the analogies generated by more than 350 first-year computing students. They were provided with a code snippet and tasked to generate their own recursion-based analogies using ChatGPT, optionally including personally relevant topics in their prompts. We observed a great deal of diversity in the analogies produced with student-prescribed topics, in contrast to the otherwise generic analogies, highlighting the value of student creativity when working with LLMs. Not only did students enjoy the activity and report an improved understanding of recursion, but they described more easily remembering analogies that were personally and culturally relevant.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
