EduAgent: Generative Student Agents in Learning
This work addresses the challenge of dynamic student simulation in online education, offering a novel approach that leverages cognitive science insights for more accurate and robust simulations.
The paper tackles the problem of simulating diverse student behaviors in online education by introducing EduAgent, a generative agent framework that incorporates cognitive prior knowledge to guide large language models, resulting in the ability to mimic real student behaviors and generate realistic virtual student behaviors without real data.
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.