AI-Powered Big Data Analytics for Personalized Learning in Education

The rapid expansion of educational data necessitates the development of advanced analytics frameworks. This research proposes an innovative AI-powered big data analytics framework which is designed to enhance personalized learning in education. At the core of this framework, the novel BERTPageRank (BPR) algorithm is implemented. The proposed algorithm synergizes the natural language understanding capabilities of BERT with the ranking efficiency of PageRank. BPR effectively ranks and prioritizes educational resources by evaluating their relevance to individual student queries and their overall learning context, thereby offering highly tailored learning material. The proposed framework was evaluated through simulation analysis, employing various performance metrics such as precision, mean reciprocal rank, execution time and learning gain. The results were benchmarked against established algorithms including Latent Semantic Analysis, Hierarchical Attention Networks and Collaborative Topic Regression. BPR demonstrated superior performance in terms of precision, scalability and adaptability in diverse educational environments. This research highlights the potential of the BPR algorithm in revolutionizing personalized learning by optimizing educational resource delivery. The findings underscore the importance of integrating advanced AI and machine learning techniques in big data analytics to address the challenges of modern education.