Construction of College English Autonomous Learning Environment Based on the Integration of Internet of Things and Edge Computing

The rapid growth of technology has transformed educational environments, particularly for language learners. Integrating the Internet of Things (IoT) with edge computing (EC) offers the potential for real-time, personalized learning experiences in college English education. However, few intelligent and adaptive systems effectively tailor instruction based on real-time data, and traditional methods struggle to meet individual learner needs. The research proposes a Smart Optimizer-fused Dynamic Elman Neural Network (SO-DENN) to create an autonomous English learning environment. Real-time data on student engagement and performance are collected through IoT infrastructure, while EC enables local data processing to reduce latency and support timely interventions. Z-score normalization and Principal Component Analysis (PCA) are employed for data preprocessing and feature extraction. The SO-DENN model dynamically adjusts content and pacing based on learner performance. Experimental results demonstrate high accuracy (98%), recall (91.6%), F1 score (93.2%), precision (92%), specificity (90.6%), and sensitivity (91.7%). Compared to conventional models, the proposed approach significantly enhances student engagement and learning efficiency. These findings suggest that IoT and EC integration can foster more adaptive and effective educational environments.