Adaptive Hierarchical Reinforcement Learning with Probabilistic Decision Networks for Personalized Learning

This research investigates the application of Adaptive Hierarchical Reinforcement Learning with Probabilistic Decision Networks (AHRL-PDN) within personalized learning systems. AHRL-PDN’s performance is meticulously compared with a wide range of dynamic learning path models, including Reinforcement Learning (RL), Bayesian Knowledge Tracing (BKT), Markov Decision Processes (MDPs), Neural Networks, Genetic Algorithms, Fuzzy Logic Systems, and Ant Colony Optimization (ACO). The study involves simulations with 50 students and 5 distinct learning objectives. It achieved the highest adaptability and scalability scores, as clearly illustrated in the accompanying comparative bar graph. Notably, AHRL-PDN demonstrated a 1.2-second response time and a $95 \%$ accuracy rate in dynamically adjusting learning paths, outperforming the other models in these key metrics. Additionally, AHRL-PDN exhibited remarkable robustness, maintaining its superior performance as the number of students was scaled up to 200 and the learning objectives expanded to 20. This scalability highlights the framework’s effectiveness in managing large-scale educational environments. These results underscore the significant potential of AHRL-PDN in enhancing personalized learning experiences by enabling rapid, real-time adaptation to individual student feedback, thereby optimizing educational outcomes.