Semantic Web Technologies for Knowledge Graph-based Personalized E-Learning Platforms

Thesis Abstract

Abstract


This research delves into the transformative potential of Semantic Web technologies and Knowledge Graphs in shaping personalized e-learning experiences. Motivated by the dynamic nature of contemporary education, the study explores the intersections of semantic technologies, knowledge graphs, and personalization strategies within e-learning platforms. A comprehensive literature review navigates through the realms of Semantic Web standards, knowledge representation, and personalized learning paradigms, highlighting challenges and opportunities in this interdisciplinary space. The core of the research involves the development and evaluation of a Semantic E-Learning Platform, incorporating knowledge graphs for enhanced content representation and personalization algorithms to tailor learning experiences. The implementation and evaluation phases delve into integration with educational institutions, performance metrics, user experience, ethical considerations, and compliance with privacy standards. The outcomes contribute to advancing the discourse on leveraging Semantic Web technologies and Knowledge Graphs for adaptive and personalized e-learning ecosystems.