The necessity for recommendation models that can capture both semantic information and device-mediated learner interactions has increased due to the rapid growth of IoT-aware e-learning environments. IoT-enhanced learning in this context refers to intelligent learning platforms that continuously create and log heterogeneous interaction data, including session dynamics, access patterns across linked devices, and engagement behaviors. This work introduces a coherent hybrid framework that combines an Actor–Critic reinforcement learning agent optimized using Proximal Policy Optimization (PPO) with BERT-based semantic encoding. By combining textual content with context-aware interaction logs gathered from intelligent learning platforms, the method creates richer learner representations. While a Mahalanobis distance module offers correlation-aware similarity cues to enhance resilience under sparse and high-dimensional data, these representations allow the Actor–Critic agent to constantly improve its recommendation policy. The usefulness of the suggested framework for IoT-aware intelligent e-learning systems is demonstrated by experiments conducted on three public MOOC datasets, which show steady improvements over robust baselines.
Chunqin et al. (Mon,) studied this question.