Discussion: The Tension Between Informed Consent and Predictive Accuracy
The integration of predictive learning analytics within higher education exposes an unresolved tension between individual privacy rights and algorithmic validity. Under the European data protection architecture, the stringent standards governing voluntary consent mandate that data subjects retain unambiguous control over the processing of their personal information (The General Data Protection Regulation (GDPR), 2025). Furthermore, the statutory conditions for lawful data processing require specific, informed, and freely given authorization, which individuals may withdraw at any stage (Article 7 Conditions for consent, 2020). While these regulatory safeguards reinforce fundamental privacy protections and govern global analytics practices (The EU General Data Protection Regulation (GDPR), 2023), they simultaneously induce systemic self-selection bias within educational datasets. When students systematically choose whether to permit data processing, the resulting cohorts diverge non-randomly from the general student body, thereby distorting dropout-prediction models and eroding their internal validity. Current scholarship extensively evaluates legal compliance frameworks and institutional accountability, yet it largely overlooks the empirical threshold where consent-induced sample attrition impairs algorithmic generalizability. This research gap obscures the practical trade-offs between legal fidelity and predictive accuracy in automated intervention systems. A significant limitation of this analysis rests on its reliance on theoretical and statutory interpretations without access to proprietary institutional retention data across distinct legal jurisdictions. Consequently, addressing this methodological schism requires further empirical investigation into alternative processing bases, such as public interest or legitimate institutional interest, to reconcile statistical integrity with European data governance standards.