A DYNAMIC MODEL FOR PERSONALIZED E-LEARNING USING INTERNET OF THINGS

Abstract

Personalized Online Learning is an adaptive educational approach that tailors learning experiences based on individual learner needs, preferences, and progress. It utilizes artificial intelligence, machine learning, and big data analytics to customize teaching and learning services, thus enhancing academic performance. Traditional POL systems predominantly rely on limited dynamic learner profiles based on behavioral and historical data. They struggle to provide real-time adaptation and context-aware personalization by disregarding critical dynamic factors such as cognitive load, emotional state, and environmental conditions. This dissertation proposes an innovative Internet of Things (IoT) based personalized e-learning framework that integrates real-time learner data, addressing the limitations of current approaches in adaptive e-learning. The research introduces a novel IoT-based dynamic model for personalized e-learning, comprising five modules: (1) a prior knowledge classification and clustering module that dynamically adjusts learning content level based on learners prior knowledge levels; (2) a machine learning-driven learning style preference module that customizes content formats according to individual learning styles; (3) an IoT module that integrates environmental and biological data; (4) a customized Learning Object Container that enriches learning objects with additional metadata; and (5) a rule-based Smart Engine that manages and integrates all modules to personalize learning materials dynamically. The framework is implemented within the Moodle learning management system, and a pilot case study is conducted to evaluate its effectiveness. The study addresses significant challenges, including prior knowledge classification and clustering model, new learning style classification model, real-time data processing, and integration of IoT devices in POL systems. It also explores pedagogical considerations such as cognitive load management, adaptive assessment and feedback, and interactive learning strategies. Empirical validation demonstrates the model’s effectiveness in improving engagement, knowledge retention, and academic performance through data-driven, context-aware adaptation. This research advances the field of IoT-based educational technologies by offering a scalable, intelligent framework that transforms online learning into a dynamic learner-centered experience. Future research directions include artificial intelligence enhanced personalization, scalability in large-scale deployments, and integration with emerging educational technologies.