AI RESEARCH
Synthetic Data in Education: Empirical Insights from Traditional Resampling and Deep Generative Models
arXiv CS.LG
•
ArXi:2604.21031v1 Announce Type: new Synthetic data generation offers promise for addressing data scarcity and privacy concerns in educational technology, yet practitioners lack empirical guidance for selecting between traditional resampling techniques and modern deep learning approaches. This study presents the first systematic benchmark comparing these paradigms using a 10,000-record student performance dataset.