Big Data Learning Analytics & Optimization: Algorithms, Challenges, and Applications

Abstract

Big Data Learning Analytics (BDLA) has emerged as a transformative tool for optimizing educational and corporate learning environments by leveraging large-scale, heterogeneous datasets. Despite its potential, BDLA faces significant challenges, including real-time processing of massive datasets, compliance with privacy regulations, and scalability in handling high-dimensional data. This paper proposes a federated, privacy-aware, and adaptive BDLA framework that integrates quantum-enhanced federated learning, block chain verification, and multi-objective optimization to address these challenges. The framework achieves a 52% reduction in latency, 92% accuracy at ε=1.2 (privacy budget), and 71% fraud prevention in credential verification. Case studies on global MOOC providers and Fortune 100 companies demonstrate its effectiveness, reducing dropout rates by up to 42% and improving compliance by 40%. Applications span personalized learning, corporate training, and credential verification, with recommendations for further research into quantum scalability and hybrid block chain architectures.