Data-Driven Optimization of Digital Learning Platforms

Abstract

Digital learning platforms generate rich operational data -- server response times, content delivery latency, learner interaction patterns, A/B test outcomes, and infrastructure utilisation metrics -- that can drive systematic platform optimisation to improve both learner experience and operational efficiency. Data-driven optimisation applies machine learning, statistical experimentation, and multi-objective optimisation to continuously improve platform performance across competing objectives: minimising page load time while controlling infrastructure cost; maximising content recommendation click-through rate while maintaining learning outcome quality; and optimising content delivery networks for geographic coverage while respecting GDPR data residency constraints. This paper proposes the Digital Learning Platform Optimisation Framework (DLPOF), a systematic methodology applying five optimisation techniques -- multi-armed bandit A/B testing, Bayesian optimisation of infrastructure parameters, multi-objective evolutionary optimisation of content delivery, reinforcement learning for server resource scheduling, and causal inference for platform feature impact estimation -- across eight platform optimisation objectives at two large-scale digital learning platforms. Key results: multi-armed bandit A/B testing improves content recommendation CTR by 24.8% with 68% fewer learner exposures than classical A/B; Bayesian infrastructure optimisation reduces server cost by 28.4% while maintaining SLA; causal inference estimates feature impact with +-3.2% accuracy vs. experimental ground truth. DLPOF introduces the Platform Optimisation Score (POS) and provides a practical optimisation deployment guide.