LLM使用中的引导与交互策略对学习者表现和感知的影响
Impact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perception
AI总结：
本研究通过课堂与在线实验，探究四种引导策略对学习者表现、信心和信任的影响，发现直接答案提升表现，完善方案增强信任，结构化引导减少随机查询，凸显教师作用。
摘要：
基于聊天的个性化教学助手对于应对日益扩大的课堂规模至关重要，尤其是在教师直接在场受限的情况下。大语言模型（LLM）提供了一条有前景的途径，越来越多的研究正在探索其教育效用。然而，挑战不仅在于确立LLM的有效性，还在于辨别学习者与这些模型之间交互的细微差别，这些差别会影响学习者的参与度和结果。我们在一个本科计算机科学课堂（N=145）中进行了一项形成性研究，并在Prolific平台上进行了一项受控实验（N=356），以探索四种基于教学法的引导策略对学习者在LLM上的表现、信心和信任的影响。直接提供LLM答案能略微提升表现，而完善学生解决方案则能培养信任。结构化引导减少了随机查询以及学生将作业问题复制粘贴给LLM的情况。我们的工作凸显了教师在塑造LLM支持的学习环境中可以发挥的作用。
英文摘要：
Personalized chatbot-based teaching assistants can be crucial in addressing increasing classroom sizes, especially where direct teacher presence is limited. Large language models (LLMs) offer a promising avenue, with increasing research exploring their educational utility. However, the challenge lies not only in establishing the efficacy of LLMs but also in discerning the nuances of interaction between learners and these models, which impact learners' engagement and results. We conducted a formative study in an undergraduate computer science classroom (N=145) and a controlled experiment on Prolific (N=356) to explore the impact of four pedagogically informed guidance strategies on the learners' performance, confidence and trust in LLMs. Direct LLM answers marginally improved performance, while refining student solutions fostered trust. Structured guidance reduced random queries as well as instances of students copy-pasting assignment questions to the LLM. Our work highlights the role that teachers can play in shaping LLM-supported learning environments.
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。
