Stable and Privacy-Preserving Synthetic Educational Data with Empirical Marginals: A Copula-Based Approach

TL;DR

This paper introduces NPGC, a copula-based method for generating privacy-preserving synthetic educational data that maintains marginal distributions and dependencies more reliably than existing deep learning approaches.

Contribution

The paper presents NPGC, a non-parametric, empirical approach that preserves data marginals and dependencies, supports heterogeneous variables, and integrates differential privacy for educational data synthesis.

Findings

NPGC remains stable over multiple regeneration cycles.

NPGC achieves competitive downstream performance.

NPGC is computationally more efficient than deep learning baselines.

Abstract

To advance Educational Data Mining (EDM) within strict privacy-protecting regulatory frameworks, researchers must develop methods that enable data-driven analysis while protecting sensitive student information. Synthetic data generation is one such approach, enabling the release of statistically generated samples instead of real student records; however, existing deep learning and parametric generators often distort marginal distributions and degrade under iterative regeneration, leading to distribution drift and progressive loss of distributional support that compromise reliability. In response, we introduce the Non-Parametric Gaussian Copula (NPGC), a plug-and-play synthesis method that replaces deep learning and parametric optimization with empirical statistical anchoring to preserve the observed marginal distributions while modeling dependencies through a copula framework. NPGC integrates Differential Privacy (DP) at both the marginal and correlation levels, supports heterogeneous variable types, and treats missing data as an explicit state to retain informative absence patterns. We evaluate NPGC against deep learning and parametric baselines on five benchmark datasets and demonstrate that it remains stable across multiple regeneration cycles and achieves competitive downstream performance at substantially lower computational cost. We further validate NPGC through deployment in a real-world online learning platform, demonstrating its practicality for privacy-preserving research.