计算专业学生如何与大型语言模型交互用于学习目的的实证研究
An Empirical Study on how Computing Students Interact with Large Language Models for Learning Purposes
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中文总结 AI 辅助
本研究基于自我调节学习理论，调查了225名计算专业学生使用大型语言模型的学习策略、感知影响与困难，并提出“探测-监控-塑形”启发式方法以映射交互策略至SRL阶段。
中文摘要 AI 辅助
大型语言模型（LLM）已成为教育领域的流行工具。因此，理解学生如何将LLM用于学习目的，是负责任地设计和开发基于LLM的学习工具的重要方面。然而，现有研究侧重于评估模型性能、特定用例以及学生对LLM在教育中应用的态度。在计算教育领域，关于学生与LLM交互策略的实证工作有限。本研究借鉴自我调节学习（SRL）理论，对225名本科和研究生计算专业学生进行了调查，以探究学生在学习过程中如何使用LLM工具、感知的学习影响以及使用这些工具时所依赖的策略。学生使用LLM涉及九个功能类别，最常用于解释概念、学习新知识、查找信息、调试和生成想法。他们还采用了一些策略，如提供详细上下文和迭代提示。使用LLM工具时最常见的困难是信息错误、回答泛泛以及难以信任模型的推理。我们通过SRL视角综合这些模式，并提出了一种“探测-监控-塑形”启发式方法，将学生的LLM交互策略映射到自我调节学习的各个阶段。
英文摘要
Large Language Models (LLM) have become popular tools in education. Therefore, understanding how students interact with LLMs for learning purposes is an important aspect of the responsible design and development of LLM based learning tools. However, existing research has focused on evaluating model performance, specific use cases, and students' attitudes toward LLM in education. There has been limited empirical work exploring students' interaction strategies with LLMs in computing education. This study draws on Self Regulated Learning (SRL) theory and conducts a survey of 225 undergraduate and graduate computing students to investigate how students use LLM tools during learning, the perceived learning impact, and the strategies they rely upon when using these tools. Students used LLMs across nine functional categories, most often to explain concepts, learn new material, find information, debug, and generate ideas. They also adopted some strategies such as supplying detailed context and iterative prompting. The most common difficulties encountered with LLM tools were incorrect information, generic responses, and difficulty trusting the model's reasoning. We synthesized these patterns through an SRL lens and offer a Probe Monitor Shape heuristic to map students' LLM interaction strategies onto the phases of self-regulated learning.
发表机构
- University of Cincinnati（辛辛那提大学）
机构由 AI 辅助整理，请以论文原文为准。
