Cyclic Adaptive Private Synthesis for Sharing Real-World Data in Education

2026-02-09

AI Summary

This study addresses the challenges of sharing and reusing real-world educational data (RWD), which are often hindered by high dimensionality, limited sample sizes, and stringent privacy constraints. To overcome these limitations, the authors propose the Cyclic Adaptive Private Synthesis (CAPS) framework, which introduces a cyclic adaptive mechanism into differentially private synthetic data generation for educational RWD. By iteratively generating synthetic data and refining it through feedback loops, CAPS enhances data utility while maintaining rigorous privacy guarantees. Experimental results on real educational datasets demonstrate that CAPS significantly outperforms one-shot synthetic baselines, achieving a superior trade-off between privacy preservation and data utility. This approach offers a viable pathway for advancing open science and design-based research in education under strict privacy requirements.

Technology Category

• Machine Learning: Privacy
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

The rapid adoption of digital technologies has greatly increased the volume of real-world data (RWD) in education. While these data offer significant opportunities for advancing learning analytics (LA), secondary use for research is constrained by privacy concerns. Differentially private synthetic data generation is regarded as the gold-standard approach to sharing sensitive data, yet studies on the private synthesis of educational data remain very scarce and rely predominantly on large, low-dimensional open datasets. Educational RWD, however, are typically high-dimensional and small in sample size, leaving the potential of private synthesis underexplored. Moreover, because educational practice is inherently iterative, data sharing is continual rather than one-off, making a traditional one-shot synthesis approach suboptimal. To address these challenges, we propose the Cyclic Adaptive Private Synthesis (CAPS) framework and evaluate it on authentic RWD. By iteratively sharing RWD, CAPS not only fosters open science, but also offers rich opportunities of design-based research (DBR), thereby amplifying the impact of LA. Our case study using actual RWD demonstrates that CAPS outperforms a one-shot baseline while highlighting challenges that warrant further investigation. Overall, this work offers a crucial first step towards privacy-preserving sharing of educational RWD and expands the possibilities for open science and DBR in LA.

Problem

• real-world data
• privacy-preserving sharing
• educational data
• differential privacy
• learning analytics

Innovation

• Cyclic Adaptive Private Synthesis
• differentially private synthetic data
• real-world educational data
• iterative data sharing
• design-based research