GDPR Limits on Learning Analytics in Public HE

Algorithmic monitoring within tertiary education institutions increasingly depends on granular student interaction logs to optimize academic support and predict academic progression. However, institutional deployment of these analytical tools encounters stringent European regulatory standards that protect individual privacy rights and govern institutional data stewardship [2]. Ensuring compliance requires reconciling institutional pedagogical objectives with statutory data protection mandates across public sector universities [3].\n\nA core operational tension arises from the conflict between comprehensive tracking paradigms and foundational data minimization principles established under modern statutory frameworks [5]. Public higher education authorities frequently encounter ambiguity when establishing appropriate lawful processing grounds, particularly between explicit consent and public task execution. Furthermore, automated profiling triggers specific obligations regarding algorithmic transparency, explainability, and individual access controls that existing educational technology infrastructures struggle to maintain systematically [1].\n\nThis study examines the legal and institutional limits imposed by European data protection law on learning analytics deployments within public universities. Utilizing qualitative comparative legal analysis and policy evaluation methodologies, the investigation assesses regulatory documentation alongside technical compliance frameworks [6]. The resulting analysis clarifies the permissible operational boundaries for institutional analytics, establishing guidelines for privacy-preserving data governance and automated decision oversight across public higher education environments.\n\nBy categorizing key compliance risks across the student data lifecycle, the analysis establishes clear benchmarks for evaluating algorithmic interventions. Reconciling technical processing architectures with fundamental individual rights provides educational leaders and systems engineers with a robust framework for ethical data management. This approach ensures accountability while preserving the educational utility of predictive modeling tools.