用真实学习数据训练生成式学生代理，模拟课堂行为动态变化。
Classroom Simulacra: Building Contextual Student Generative Agents in Online Education for Learning Behavioral Simulation
- 设计可迭代反思模块，增强大模型对长文本课程内容的理解与行为模拟。
- 在60名学生6周的实证数据上验证，模型在少量示范下表现优于传统方法。
- 适合教育技术研究者和智能教学系统开发者参考。
学生模拟有助于教师优化教学，但现有方法因缺乏细粒度标注的课程材料数据集，且模型难以处理超长文本，常忽略课程内容对学习行为的影响。为此，我们基于60名学生参与的6周在线教育实验，通过自建系统采集了学生与课件互动的细粒度学习行为日志。提出可迁移的迭代反思（TIR）模块，增强提示工程与微调类大语言模型在学习行为模拟中的能力。实验表明，即使在有限示范数据下，TIR仍显著提升模拟精度，更准确捕捉学习表现的细粒度动态与学生间关联性，为在线教育‘数字孪生’提供可行路径。
原文摘要 · Abstract (English)
Student simulation supports educators to improve teaching by interacting with virtual students. However, most existing approaches ignore the modulation effects of course materials because of two challenges: the lack of datasets with granularly annotated course materials, and the limitation of existing simulation models in processing extremely long textual data. To solve the challenges, we first run a 6-week education workshop from N = 60 students to collect fine-grained data using a custom built online education system, which logs students' learning behaviors as they interact with lecture materials over time. Second, we propose a transferable iterative reflection (TIR) module that augments both prompting-based and finetuning-based large language models (LLMs) for simulating learning behaviors. Our comprehensive experiments show that TIR enables the LLMs to perform more accurate student simulation than classical deep learning models, even with limited demonstration data. Our TIR approach better captures the granular dynamism of learning performance and inter-student correlations in classrooms, paving the way towards a ''digital twin'' for online education.
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