Enhancing computer education through IoT-Enabled learning environments leveraging mobile edge computing for real-time feedback
Xin Yu, Yinghua Tian · Systems and Soft Computing · 2025
The integration of Internet of Things (IoT) technologies into educational systems has opened new pathways for enhancing teaching effectiveness and student engagement, particularly in the domain of computer education. Traditional computer education methods often face limitations in engagement monitoring, timely feedback delivery, and personalized instruction. To address these challenges, this research proposes an intelligent digital learning framework that enhances computer education through the integration of IoT devices and Mobile Edge Computing (MEC), enabling real-time feedback and adaptive instruction. In the proposed framework, IoT-enabled sensors collect real-time multimodal data reflecting students’ engagement and interaction within the classroom. These data streams are offloaded to local MEC nodes, where lightweight deep learning models process the information rapidly, eliminating reliance on cloud-based latency. The Dynamic Elephant Herding Optimiser-driven Feedback Stacked Long Short-Term Memory (DEH-FStacked LSTM) network processes this data at the edge to detect behavioural cues and assess teaching quality. Simultaneously, the module learns optimal feedback policies based on student state transitions, guided by a reward function that maximizes learning effectiveness while minimizing response delay. By deploying these deep learning models on edge servers, the system ensures ultra-low latency, maintains data privacy, and delivers personalized, real-time interventions. Experimental results demonstrate overall performance above 92 %, highlighting its reliability, responsiveness, and practical value for real-world educational environments. The proposed method has reached the highest accuracy of 94.32; compared to other models like Stacked LSTM (91.2 %), Bi-LSTM (92.0 %), CNN-LSTM (89.7 %), and Transformer- based architectures (93.1 %). It also provides better precision, recall and F1-score and is shown to have a visible and quantifiable improvement on the current State of the Art of engagement prediction in real-time. This research validates that incorporating deep learning within an IoT and MEC-based infrastructure significantly enhances the quality, responsiveness, and overall effectiveness of computer education in higher learning institutions.