🤖 AI Summary
This study addresses the prevailing overemphasis on legal compliance in student data governance within learning analytics, which often neglects critical ethical dimensions such as fairness, student autonomy, accountability, and educational purpose. To bridge this gap, the work proposes LEAGUE—a six-pillar ethical governance framework encompassing Legitimacy, Equity, Autonomy, Governance, Utility, and Ethics-by-Design—integrating insights from learning analytics, data ethics, and capability-oriented theories of educational justice. Moving beyond conventional compliance paradigms, this framework pioneers the application of value-sensitive design in the field. Through conceptual review, interdisciplinary theoretical synthesis, and a case analysis of an early warning system, the study demonstrates the framework’s feasibility in enhancing transparency, educational meaningfulness, and ethical justifiability, offering a theoretically grounded yet practically actionable pathway for ethically robust learning analytics.
📝 Abstract
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.