Multi-access edge computing scheduling optimization model for remote education under 6G network environment based on reinforcement learning

Abstract The evolution of digital education necessitates robust computational frameworks to address the complexities inherent in remote learning environments. Traditional scheduling mechanisms often fall short in accommodating the dynamic nature of learner engagement and the asynchronous delivery of content. To bridge this gap, we introduce a novel computational model that leverages reinforcement learning to optimize content delivery schedules. Central to our approach is the Attentive Stochastic Transition Estimation Network (ASTEN), which models the probabilistic transitions of learner states, accounting for factors such as attention variability and feedback delays. Complementing ASTEN is the Selective Informative Delivery Strategy (SIDS), a decision-theoretic framework that determines optimal content emission based on real-time uncertainty assessments and pedagogical utility. Our approach captures nuanced behavioral trends, such as sporadic learner interaction, temporal learning decay, and individualized attention cycles, thereby enabling a more responsive and tailored instructional strategy. By explicitly integrating cognitive and behavioral signals within the scheduling framework, our model facilitates the delivery of content that aligns with each learner’s evolving state. Empirical evaluations demonstrate that our integrated model significantly enhances learning outcomes by adapting to individual learner trajectories and mitigating the challenges posed by sparse feedback. This research contributes to the theoretical foundations of computational learning models and offers practical insights for the development of adaptive educational technologies, particularly in environments where traditional one-size-fits-all approaches prove inadequate.