利用大语言模型大规模支持自我反思：来自课堂随机现场实验的启示
Supporting Self-Reflection at Scale with Large Language Models: Insights from Randomized Field Experiments in Classrooms
AI总结：
本文通过两项本科课堂随机现场实验，研究大语言模型辅助课后自我反思的效果，发现LLM引导的反思能提升学生自信心与考试成绩，效果与问卷活动相当且优于单纯复习幻灯片，表明LLM在增强元认知技能方面具有重要潜力。
摘要：
对学习经历的自我反思构成了一个基本的认知过程，对于知识巩固和学习效能提升至关重要。然而，传统的促进反思方法在个性化、反馈即时性、参与度和可扩展性方面常常面临挑战。将大语言模型（LLMs）整合到反思过程中可以缓解这些局限性。在本文中，我们在本科计算机科学课程中进行了两项随机现场实验，以调查LLMs帮助学生进行课后反思的潜力。在第一项实验（N=145）中，学生在LLM助手的支持下完成了一项课后作业；随后，其中一半学生获得了旨在促进自我反思的LLM的访问权限。结果表明，被分配进行LLM引导反思的学生报告称自信心有所增强，并且在两周后的后续考试中表现优于控制条件下的同伴。在第二项实验（N=112）中，我们在作业完成后，评估了LLM引导的自我反思与其他可扩展反思方法（如基于问卷的活动和复习关键讲座幻灯片）相比的影响。我们的研究结果表明，根据两周后针对相同主题的监考考试成绩，问卷和基于LLM的反思组中的学生表现同样出色，并且优于那些仅接触讲座幻灯片的学生。这些结果突显了LLM引导的反思和基于问卷的活动在改善学习成果方面的效用。我们的工作强调，仅仅关注LLM的准确性可能会忽视它们通过自我反思等实践来增强元认知技能的潜力。我们讨论了我们的研究对教育科技社区的影响。
英文摘要：
Self-reflection on learning experiences constitutes a fundamental cognitive process, essential for the consolidation of knowledge and the enhancement of learning efficacy. However, traditional methods to facilitate reflection often face challenges in personalization, immediacy of feedback, engagement, and scalability. Integration of Large Language Models (LLMs) into the reflection process could mitigate these limitations. In this paper, we conducted two randomized field experiments in undergraduate computer science courses to investigate the potential of LLMs to help students engage in post-lesson reflection. In the first experiment (N=145), students completed a take-home assignment with the support of an LLM assistant; half of these students were then provided access to an LLM designed to facilitate self-reflection. The results indicated that the students assigned to LLM-guided reflection reported increased self-confidence and performed better on a subsequent exam two weeks later than their peers in the control condition. In the second experiment (N=112), we evaluated the impact of LLM-guided self-reflection against other scalable reflection methods, such as questionnaire-based activities and review of key lecture slides, after assignment. Our findings suggest that the students in the questionnaire and LLM-based reflection groups performed equally well and better than those who were only exposed to lecture slides, according to their scores on a proctored exam two weeks later on the same subject matter. These results underscore the utility of LLM-guided reflection and questionnaire-based activities in improving learning outcomes. Our work highlights that focusing solely on the accuracy of LLMs can overlook their potential to enhance metacognitive skills through practices such as self-reflection. We discuss the implications of our research for the Edtech community.
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