用大模型和CTGAN生成虚拟学生数据，解决学习分析中的隐私难题。
Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation
- 结合CTGAN与GPT2等大模型生成模拟学生表格式数据。
- 生成数据在统计与预测性能上接近真实数据，验证了可行性。
- 适合关注数据隐私、学习分析研究者使用。
本研究探索生成对抗网络（GAN）与大型语言模型（LLM）在生成合成表格数据方面的潜力。学习分析的发展依赖高质量的学生数据，但隐私顾虑和全球严格的数据保护法规限制了其获取与使用。合成数据为此提供可行替代方案。本文研究合成数据是否可用于构建虚拟学生以支持学习分析模型。我们采用流行的GAN模型CTGAN及三种LLM（GPT2、DistilGPT2、DialoGPT）生成合成学生表格数据。结果表明，这些方法能生成高质量、贴近真实数据的合成数据集。通过一系列综合评估指标，我们检验了合成数据的统计与预测性能，并对比不同生成模型的表现，尤其关注LLMs的效能。本研究旨在为学习分析领域提供关于合成数据应用的宝贵见解，推动该领域方法论工具箱的创新拓展。
原文摘要 · Abstract (English)
In this study, we explore the growing potential of AI and deep learning technologies, particularly Generative Adversarial Networks (GANs) and Large Language Models (LLMs), for generating synthetic tabular data. Access to quality students data is critical for advancing learning analytics, but privacy concerns and stricter data protection regulations worldwide limit their availability and usage. Synthetic data offers a promising alternative. We investigate whether synthetic data can be leveraged to create artificial students for serving learning analytics models. Using the popular GAN model CTGAN and three LLMs- GPT2, DistilGPT2, and DialoGPT, we generate synthetic tabular student data. Our results demonstrate the strong potential of these methods to produce high-quality synthetic datasets that resemble real students data. To validate our findings, we apply a comprehensive set of utility evaluation metrics to assess the statistical and predictive performance of the synthetic data and compare the different generator models used, specially the performance of LLMs. Our study aims to provide the learning analytics community with valuable insights into the use of synthetic data, laying the groundwork for expanding the field methodological toolbox with new innovative approaches for learning analytics data generation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。