超越合规：学习分析中学生数据伦理治理的拟议框架
Beyond Compliance: A Proposed Framework for Ethical Governance of Student Data in Learning Analytics
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中文总结 AI 辅助
针对学习分析学生数据治理仅重合规的问题，提出LEAGUE六支柱伦理治理框架，通过案例展示其可助力机构更透明地开展学习分析审查。
中文摘要 AI 辅助
高等教育中学习分析（LA）的快速发展，提升了机构利用学生数据监测参与度、预测学业困难及定向提供支持的能力。这些实践虽带来重要教育效益，但治理常以合规为首要，聚焦于满足《家庭教育权利和隐私法》（FERPA）、《通用数据保护条例》（GDPR）等基线法律要求。法律合规是必要的，但本身无法解决公平性、学生自主权、问责制或教育目标等问题。本文提出LEAGUE框架，这是学习分析中学生数据伦理治理的六支柱模型：合法性（Lawfulness）、公平性（Equity）、自主权（Agency）、治理（Governance）、效用（Utility）和设计伦理（Ethics by Design）。该框架通过对学习分析、教育数据挖掘、数据伦理、教育政策、价值敏感设计及以能力为导向的教育正义方法的学术成果进行概念综合而开发。其实际价值通过一个示例早期预警案例得以展示，该案例表明机构如何以更透明、更具教育意义的方式审查学习分析。
英文摘要
The rapid growth of learning analytics (LA) in higher education has expanded institutional capacity to monitor engagement, predict academic difficulty, and target support using student data. While these practices offer important educational benefits, governance has often remained compliance-first, centered on meeting baseline legal requirements such as the Family Educational Rights and Privacy Act (FERPA) and the General Data Protection Regulation (GDPR). Legal compliance is necessary, but it does not by itself resolve questions of fairness, student agency, accountability, or educational purpose. This paper proposes the LEAGUE framework, a six-pillar model for ethical governance of student data in LA: Lawfulness, Equity, Agency, Governance, Utility, and Ethics by Design. The framework is developed through a conceptual synthesis of scholarship in learning analytics, educational data mining, data ethics, educational policy, value-sensitive design, and capability-oriented approaches to educational justice. Its practical value is demonstrated through an illustrative early alert case that shows how institutions can review learning analytics in a more transparent and educationally meaningful way.
