Hybrid Reinforcement and Evolutionary Learning Model for Adaptive Pathway Optimization In Computer Networks Education

Abstract

This paper introduces a Hybrid Reinforcement and Evolutionary Learning Model developed to optimize adaptive learning pathways in computer network education. Traditional uniform curricula often struggle to accommodate diverse learner profiles, resulting in knowledge gaps across hierarchical concepts such as OSI layers, routing protocols, and security mechanisms. The proposed model integrates Deep Knowledge Tracing (DKT) with Long Short Term Memory (LSTM) networks for real-time estimation of learners’ knowledge states, Proximal Policy Optimization (PPO) for dynamic sequential content selection, and a Genetic Algorithm Particle Swarm Optimization (GA–PSO) hybrid for global pathway refinement under constraints such as prerequisites and time limits. The model was evaluated using real learner data from an e-learning platform and achieved an average final mastery score of 0.867, quiz accuracy of 0.822, and an F1-score of 0.880 for path recommendations outperforming baseline models such as static curricula (0.740 mastery) and DKT+PPO (0.824 mastery) by 5–17%. Ablation studies validated the synergistic contribution of each component, with the GA–PSO module enhancing optimization efficiency by approximately 10%. Overall, these findings demonstrate that the proposed model offers superior personalization, learning efficiency, and adaptability, marking a significant advancement in AI-driven education for computer networks.