Abstract:

The rapid growth of learning analytics (LA) in higher education has led to a large amount of student data. This data enables educators and administrators to monitor student engagement, predict academic performance, identify at-risk students, and customize instruction at scale. While these capabilities promise significant educational benefits, the swift rise in data collection and algorithm-driven decision-making has outpaced the creation of clear ethical governance structures. As a result, institutions often adopt a compliance-first approach, meeting only the minimum standards of data protection laws like the General Data Protection Regulation (GDPR) or the Family Educational Rights and Privacy Act (FERPA) without truly addressing the deeper ethical dimensions of student data use. Compliance while necessary, is insufficient when student wellbeing, equity, and autonomy are at stake.

This paper introduces the LEAGUE Framework, a six-pillar model for the ethical governance of student data in learning analytics systems. The pillars are: Lawfulness, ensuring adherence to relevant data protection laws; Equity, supporting data practices that do not unfairly disadvantage students based on demographics or socioeconomic status; Agency, allowing students control over their data through clear opt-in processes and informed consent; Governance, establishing accountability structures and oversight committees within institutions; Utility, ensuring data collection is backed by measurable educational benefits; and Ethics by Design, incorporating value-sensitive principles into analytics systems from the start. Together, these pillars create a governance model that moves beyond compliance toward active ethical management.

The framework is based on a comprehensive literature review covering educational data mining, learning sciences, data ethics, and educational policy. Unlike existing models that focus on isolated parts of LA ethics, LEAGUE serves as a complete institutional audit tool that applies in various educational settings. To show its relevance, the paper compares the framework to a university case where learning management system data is used to identify at-risk students. This analysis shows how each pillar reveals distinct ethical challenges and guides practical governance choices. Additionally, a self-assessment rubric aligned with each pillar is included, enabling institutions to evaluate the ethical maturity of their analytics efforts and spot areas for improvement. Theoretical contributions come from value-sensitive design theory, capability approaches to educational equity, and new discussions on algorithmic accountability.

The impact of LEAGUE covers several areas of education. For curriculum designers, it gives direction on incorporating ethical considerations into data-driven learning spaces. For policymakers, it offers a clear framework for updating data governance policies focused on student rights. For educators, it emphasizes data literacy and ethical reasoning as essential professional skills. Governing learning analytics ethically is not just a technical or legal issue; it is an educational challenge. Institutions must closely examine whose interests their data practices support and ensure that learning analytics meets its potential in a fair, open, and human-centered manner.

Keywords:

Learning Analytics, data ethics, student data governance, LEAGUE framework, educational data mining, equity, value-sensitive design.