发表机构
Graduate School of Informatics, Kyoto University; Academic Center for Computing and Media Studies, Kyoto University（京都大学情报大学院; 京都大学学术计算与媒体研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ReLEAF是一个社会技术框架，通过两阶段数据共享（差分隐私合成数据探索分析与受控真实数据验证）弥合数据保管者与研究人员，促进可信的教育数据共享，并提炼出三项设计原则。
AI 中文摘要
教育领域真实世界数据（ERWD）的收集量正随着学习平台和机构系统的普及而不断增长。在学习分析社区内共享这些数据提供了可观的研究机会，然而，由于伦理、法规和治理方面的限制，数据访问仍然受限。先前的工作主要集中于匿名化技术，但对实际ERWD共享的运营设计关注甚少，尤其是数据保管者与研究人员如何通过隐私保护访问机制进行互动。为弥补这一空白，我们提出了ReLEAF，一个社会技术框架，通过实施两阶段数据共享来弥合数据保管者与研究人员之间的鸿沟：1）共享差分隐私合成数据以进行探索性分析，2）按需进行受控的真实数据验证。遵循设计科学研究方法，我们通过三个周期对ReLEAF进行完善和形成性评估，分别涉及4名研究生、6名研究人员和90名本科生。通过这三个周期，我们提炼出三项设计原则：P1）将隐私保护访问机制定位在研究工作流程中，P2）使可接受的二次使用条件明确且可操作，P3）促进对治理要求的参与，而非自动化合规决策。总之，ReLEAF为可信的ERWD共享提供了一个具体框架，同时这些设计原则为其他数据共享情境提供了可迁移的指导。
英文摘要
Growing volumes of educational real-world data (ERWD) are being collected across learning platforms and institutional systems. Sharing these data within the Learning Analytics community offers substantial research opportunities, yet access remains limited by ethical, regulatory and governance constraints. Prior work has primarily focused on anonymisation techniques, but little attention has been paid to operational design of practical ERWD sharing, particularly how data custodians and researchers interact through privacy-preserving access mechanisms. To address this gap, we propose ReLEAF, a socio-technical framework that bridges data custodians and researchers by operationalising two-stage data sharing: 1) Differentially private synthetic data are shared for exploratory analysis, and 2) controlled real-data validation is performed on demand. Following a design-science research approach, we refine and formatively evaluate ReLEAF through three cycles involving 4 graduate students, 6 researchers, and 90 undergraduate students, respectively. Three design principles emerged through the cycles: P1) position privacy-preserving access mechanisms within the research workflow, P2) make the conditions for acceptable secondary use explicit and actionable, and P3) promote engagement with governance requirements rather than automate compliance decisions. Together, ReLEAF provides a concrete framework for trustworthy ERWD sharing, while the design principles offer transferable guidance for other data-sharing contexts.
Comments17 pages, 11 figures, 4 tables
