Beyond Compliance: A Proposed Framework for Ethical Governance of Student Data in Learning Analytics

Synthesis: Beyond Compliance: A Proposed Framework for Ethical Governance of Student Data in Learning Analytics — Proposes LEAGUE framework (Lawfulness, Equity, Agency, Governance, Utility, Ethics by Design) for ethical governance of student data in learning analytics. Synthesizes scholarship across LA, educational data mining, data ethics, educational policy, v... Learning Analytics Privacy Equity Ethics AI Regulation in Education Higher Education

Proposes LEAGUE framework (Lawfulness, Equity, Agency, Governance, Utility, Ethics by Design) for ethical governance of student data in learning analytics. Synthesizes scholarship across LA, educational data mining, data ethics, educational policy, value-sensitive design, and capability-oriented approaches to educational justice. Demonstrates practical value through an illustrative early-alert case study showing how institutions can review LA practices in a more transparent and educationally meaningful way.

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 FERPA and GDPR. 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.

What this means for practice

• Administrators. Run every learning analytics initiative — including LLM, agentic, and generative student-facing systems — through the same six-pillar review (Lawfulness, Equity, Agency, Governance, Utility, Ethics by Design) before deciding whether to approve, revise, pilot, or pause it.
• Administrators. Schedule reassessment rather than treating approval as final, covering model performance and drift as well as the original educational rationale.
• Policymakers. Treat FERPA and GDPR compliance as the floor for such review: the framework adds equity, student Learner Agency, institutional accountability, and educational utility to a compliance check.
• Researchers. Build the quantitative governance-maturity rubric the framework still lacks, so institutions can score and track ethical governance over time instead of judging it case by case.

Limitations

• The framework is a conceptual model, not an empirically validated instrument, and its authors state it is not a substitute for technical fairness testing or legal counsel.
• Its worked demonstration is a single illustrative early-alert case study, not application across diverse institutions.
• Jurisdictional reach is bounded by the FERPA and GDPR contexts emphasized; international applicability is untested.
• No validated instrument or scoring rubric yet exists to measure governance maturity, so the six pillars cannot be compared quantitatively across institutions.

Citation

Sahana Varadaraju, & Bharathwaj Vijayakumar (2026). Beyond Compliance: A Proposed Framework for Ethical Governance of Student Data in Learning Analytics. EDULEARN26 Proceedings (IATED, 2026).