Automated Adaptive Mobile Learning System using the Semantic Web
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Authors
Hamada, Samir E.
Alshalabi, Ibrahim Alkore
Elleithy, Khaled M.
Badara, Joanna A.
Issue Date
2016-04
Type
Article
Language
en_US
Keywords
Adaptive learning , dotNetRDF , Graph , Mobile learning , Ontology , Resource description framework (RDF) , Semantic web , Shortest path , Turtle , User profile
Alternative Title
Abstract
ELearning (Electronic Learning) and m-learning (Mobile Learning) systems are online learning platforms. In our research we are modeling them as a weighted directed graph where each node represents a course unit. A directed graph represents an accurate picture of course descriptions for online courses through the computer-based implementation of various educational systems. The Learning Path Graph (LPG) represents and describes the structure of domain knowledge, including the learning goals, and all other available learning paths. In this paper, we propose an adaptive m-learning system architecture and a conceptual framework that uses the Semantic Web to obtain the students’ data from other educational institutions. This process will enable the educational institutions to communicate and exchange students’ data, and then use this information to adjust the students’ profiles and modify their learning paths. The Semantic Web will create a more personalized dynamic course for individual students according to their ability, educational level, and experience.
Description
Citation
Publisher
IEEE