REFINE：现实世界中交互反馈与学生行为的探索
REFINE: Real-world Exploration of Interactive Feedback and Student Behaviour
AI总结：
本文提出REFINE系统，通过多智能体反馈机制实现高效互动反馈，提升学生参与度与反馈质量。
摘要：
形成性反馈是有效学习的核心，但提供及时、个性化的反馈仍具挑战。本文介绍REFINE，一种基于小型开源LLM的本地部署多智能体反馈系统，将反馈视为互动过程。REFINE结合教育导向的反馈生成代理与人类对齐的判断引导再生循环，以及支持学生后续问题的自我反思工具调用交互代理。通过受控实验和真实课堂部署评估，自动评估显示判断引导再生显著提升反馈质量，交互代理生成的响应效率和质量与顶级闭源模型相当。学生互动分析揭示了不同的参与模式，表明系统生成的反馈系统地引导后续学生提问。研究结果证明了多智能体、工具增强反馈系统在可扩展互动反馈中的可行性和有效性。
英文摘要：
Formative feedback is central to effective learning, yet providing timely, individualised feedback at scale remains a persistent challenge. While recent work has explored the use of large language models (LLMs) to automate feedback, most existing systems still conceptualise feedback as a static, one-way artifact, offering limited support for interpretation, clarification, or follow-up. In this work, we introduce REFINE, a locally deployable, multi-agent feedback system built on small, open-source LLMs that treats feedback as an interactive process. REFINE combines a pedagogically-grounded feedback generation agent with an LLM-as-a-judge-guided regeneration loop using a human-aligned judge, and a self-reflective tool-calling interactive agent that supports student follow-up questions with context-aware, actionable responses. We evaluate REFINE through controlled experiments and an authentic classroom deployment in an undergraduate computer science course. Automatic evaluations show that judge-guided regeneration significantly improves feedback quality, and that the interactive agent produces efficient, high-quality responses comparable to a state-of-the-art closed-source model. Analysis of real student interactions further reveals distinct engagement patterns and indicates that system-generated feedback systematically steers subsequent student inquiry. Our findings demonstrate the feasibility and effectiveness of multi-agent, tool-augmented feedback systems for scalable, interactive feedback.
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。
