Edge-Based Personalized Tutoring Framework for Adaptive Educational Support in Remote Mountain Communities
DOI:
https://doi.org/10.22399/ijcesen.5490Keywords:
Edge Computing, Personalized Tutoring System, Adaptive Learning, Deep Learning, Intelligent Tutoring Systems, Remote Mountain CommunitiesAbstract
Educational inequality in remote mountain communities remains a major challenge due to inadequate digital infrastructure, poor internet connectivity, shortage of qualified teachers, and limited access to adaptive learning technologies. Due to their reliance on constant network connectivity and central processing, traditional cloud-based e-learning systems frequently fail to deliver the desired results in geographically separated places. To address these limitations, this research proposes an edge-based personalized tutoring model for adaptive educational support in remote and real-time learner analytics to deliver intelligent, low-latency, and personalized educational support in resource-constrained environments. Learning dataset is used for Remote Mountain Communities, comprising 20000 samples. The methodology employs One-Hot encoding for categorical learner attributes, IQR-based outlier detection for data cleaning, Autoencoder for feature extraction to capture engagement, cognitive load, and learning trends. A Bi-RNN-GELS model is then applied, where Bi-RNN captures bidirectional temporal learning patterns and GELS optimizes personalized learning paths by treating each strategy as a particle influenced by better solutions. The Experimental setup uses Python 3.10 with TensorFlow and PyTorch. Experimental results demonstrated significant improvements with 96.3% accuracy, 72.5ms latency, and 0.6s average response time, along with enhanced learner engagement and reduced dropout rates compared to conventional cloud-centric tutoring systems. This research provides a scalable, explainable, and resilient edge-based solution for bridging the digital education divide and enabling inclusive AI-driven learning ecosystems in underserved remote mountain communities.
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