Discussion: Balancing Pedagogical Innovation and Fundamental Privacy Rights
The intersection of algorithmic learning analytics and statutory compliance reveals significant friction within internationalized educational settings. While higher education institutions increasingly leverage educational data mining to optimize language instruction and evaluate pedagogical outcomes [6], repurposing granular student interaction logs challenges the fundamental doctrine of purpose limitation [2]. In English-Medium Instruction contexts, secondary processing frequently involves complex predictive models that profile learner engagement, fluency progression, and cognitive task completion without explicit, contextualized student authorization [3]. Because the original collection of data occurs strictly for direct educational delivery and assessment, extending these records to broader institutional training datasets requires either demonstrable purpose compatibility under Article 6(4) of the GDPR or a distinct legal ground [2]. Algorithmic systems deployed in these pipelines often exhibit technical opacity, obscuring the precise analytical variables that influence performance evaluations and secondary interventions [3]. Consequently, academic institutions face dual obligations: they must preserve the analytical utility of learning management systems while establishing verifiable data minimization protocols, comprehensive algorithmic accountability, and accessible notice mechanisms for multinational student bodies [3], [6].