Student simulation in online education is important to address dynamic
learning behaviors of students with diverse backgrounds. Existing simulation
models based on deep learning usually need massive training data, lacking prior
knowledge in educational contexts. Large language models (LLMs) may contain
such prior knowledge since they are pre-trained from a large corpus. However,
because student behaviors are dynamic and multifaceted with individual
differences, directly prompting LLMs is not robust nor accurate enough to
capture fine-grained interactions among diverse student personas, learning
behaviors, and learning outcomes. This work tackles this problem by presenting
a newly annotated fine-grained large-scale dataset and proposing EduAgent, a
novel generative agent framework incorporating cognitive prior knowledge (i.e.,
theoretical findings revealed in cognitive science) to guide LLMs to first
reason correlations among various behaviors and then make simulations. Our two
experiments show that EduAgent could not only mimic and predict learning
behaviors of real students but also generate realistic learning behaviors of
virtual students without real data.

该研究提出了一种新的生成代理框架 EduAgent，结合认知先验知识来引导大型语言模型首先推理各种行为之间的相关性，然后进行模拟，实现对真实学生和虚拟学生学习行为的模仿、预测和生成。

EduAgent: 学习中的生成型学生代理

EduAgent: Generative Student Agents in Learning

Student simulation presents a transformative approach to enhance learning
outcomes, advance educational research, and ultimately shape the future of
effective pedagogy. We explore the feasibility of using large language models
(LLMs), a remarkable achievement in AI, to simulate student learning behaviors.
Unlike conventional machine learning based prediction, we leverage LLMs to
instantiate virtual students with specific demographics and uncover intricate
correlations among learning experiences, course materials, understanding
levels, and engagement. Our objective is not merely to predict learning
outcomes but to replicate learning behaviors and patterns of real students. We
validate this hypothesis through three experiments. The first experiment, based
on a dataset of N = 145, simulates student learning outcomes from demographic
data, revealing parallels with actual students concerning various demographic
factors. The second experiment (N = 4524) results in increasingly realistic
simulated behaviors with more assessment history for virtual students
modelling. The third experiment (N = 27), incorporating prior knowledge and
course interactions, indicates a strong link between virtual students' learning
behaviors and fine-grained mappings from test questions, course materials,
engagement and understanding levels. Collectively, these findings deepen our
understanding of LLMs and demonstrate its viability for student simulation,
empowering more adaptable curricula design to enhance inclusivity and
educational effectiveness.

利用大型语言模型模拟学生学习行为，揭示学习行为与测试问题、课程材料、参与度和理解水平之间的关联，加深我们对大型语言模型的理解，展示其对学生模拟的可行性，从而为更具适应性的课程设计提供支持，增强包容性和教育效果。