弥合学习者众包与AI的鸿沟：探索学生与AI协作生成反馈的动态机制
Bridging Learnersourcing and AI: Exploring the Dynamics of Student-AI Collaborative Feedback Generation
AI总结：
本研究通过72名硕士生的随机对照实验，对比独立撰写与修改GPT-4生成的提示两种模式，探索AI辅助学习者众包反馈的效果与价值，为相关学习活动设计提供参考。
摘要：
本文探索了在数据科学等复杂领域中，通过结合人工智能（AI）与学习者众包这两种主流方法来优化反馈机制的方向。为应对两种方法各自带来的挑战，本研究将传统学习者众包与AI辅助方法进行了对比。我们报告了一项随机对照实验的结果，该实验以72名修读数据可视化课程的硕士生为对象，对比两种实验条件：学生独立撰写提示，与学生修改由GPT-4生成的提示。本研究旨在评估学习者众包提示的质量，检验学生表现对提示质量的影响，了解学习者对有无AI支持下撰写提示的偏好，并探索学生-AI协作练习在培养对大语言模型（LLM）的批判性思维方面的潜力。基于研究结果，我们为设计利用AI支持的学习者众包活动、以及优化学生与LLM交互过程中的学习效果提供了洞见。
英文摘要：
This paper explores the space of optimizing feedback mechanisms in complex domains, such as data science, by combining two prevailing approaches: Artificial Intelligence (AI) and learnersourcing. Towards addressing the challenges posed by each approach, this work compares traditional learnersourcing with an AI-supported approach. We report on the results of a randomized controlled experiment conducted with 72 Master's level students in a data visualization course, comparing two conditions: students writing hints independently versus revising hints generated by GPT-4. The study aimed to evaluate the quality of learnersourced hints, examine the impact of student performance on hint quality, gauge learner preference for writing hints with or without AI support, and explore the potential of the student-AI collaborative exercise in fostering critical thinking about LLMs. Based on our findings, we provide insights for designing learnersourcing activities leveraging AI support and optimizing students' learning as they interact with LLMs.
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。
