Analytical Assessment of Learning Analytics Implementations
Applying the theoretical framework of European data protection to public higher education reveals substantial structural friction between predictive learning analytics and statutory compliance. The General Data Protection Regulation fundamentally aims to provide individuals with robust control over personal data and reinforce privacy protections across all processing activities (The General Data Protection Regulation, 2025). When applied directly to learning analytics platforms that continuously process student behavioral records, engagement metrics, and academic milestones, this legal standard restricts unrestricted profiling and pervasive institutional monitoring. Public universities function as data controllers bound by mandatory duties to ensure lawful processing bases, strict purpose limitation, and rigorous data minimization across all educational environments. Consequently, the implementation of predictive algorithmic interventions in public higher education requires structured technical and institutional safeguards. Essential compliance mechanisms, specifically Privacy by Design and comprehensive Data Protection Impact Assessments, function as required frameworks for evaluating algorithmic risks prior to the integration of student monitoring tools (The EU General Data Protection Regulation, 2023). These systematic evaluations verify that automated analytical workflows remain strictly proportional to legitimate pedagogical objectives. Furthermore, because statutory ambiguity and diverse national implementations create operational uncertainties across European member states, higher education institutions must actively invest in qualified Data Protection Officers to guide compliant data governance (GDPR Ambiguity, National Diversity, 2021). Translating these core statutory duties into academic analytics ensures that predictive tools respect student rights without compromising regulatory standards.