循环自适应隐私合成用于教育领域真实世界数据的共享
Cyclic Adaptive Private Synthesis for Sharing Real-World Data in Education
AI总结：
本文提出CAPS框架，通过循环共享真实世界教育数据，提升学习分析的隐私保护与开放科学潜力。
摘要：
数字技术的快速普及显著增加了教育领域的真实世界数据（RWD）量。尽管这些数据为学习分析（LA）提供了重要机会，但其二次利用受隐私问题限制。差分隐私合成数据生成被认为是共享敏感数据的黄金标准，但关于教育数据隐私合成的研究仍十分有限，主要依赖大规模低维公开数据集。然而，教育RWD通常高维且样本小，导致隐私合成潜力未被充分挖掘。此外，由于教育实践本质上是迭代的，数据共享是持续而非一次性过程，使传统一次性合成方法不适用。为解决这些问题，我们提出循环自适应隐私合成（CAPS）框架，并在真实RWD上评估其效果。通过迭代共享RWD，CAPS不仅促进开放科学，还为基于设计的研究（DBR）提供丰富机会，从而增强LA的影响。我们的案例研究显示，CAPS在实际RWD上优于一次性基线方法，同时揭示了需要进一步研究的挑战。总体而言，本文为教育RWD的隐私保护共享提供了关键第一步，并拓展了LA领域开放科学和DBR的可能性。
英文摘要：
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.
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