Innovative Techniques for the Implementation of Adaptive Mobile Learning Using the Semantic Web
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Authors
Hamada, Samir E.
Issue Date
2016-06-16
Type
Thesis
Language
en_US
Keywords
Computer science , Adaptive learning , Mobile learning , Ontology , Semantic web
Alternative Title
Abstract
Adaptive Mobile Learning has constantly faced many challenges in order to make course learning more adaptive. This research presents a conceptual framework for using the Semantic Web to obtain students’ data from other educational institutions, enabling the educational institutions to communicate and exchange students’ data. We then can use this information to adjust the students’ profiles and modify their learning paths. Semantic Web will create a more personalized dynamic course for each student according to his/her ability, educational level, and experience. Through the Semantic Web, our goal is to create an adaptive learning system that takes into consideration previously completed courses, to count the completed topics, and then adjust the leaning path graph accordingly to get a new shortest path. We have applied the developed model on our system. Then, we tested the students on our system and a control system to measure the improvements in the students’ learning. We also have analyzed the results collected from the AML Group and the Control Group. The AML system provided a 44.80% improvement over the Control System. The experimental results demonstrate that Semantic Web can be used with adaptive mobile learning system (AML) in order to enhance the students’ learning experience and improve their academic performance.
Description
Citation
S. Hamada, "Innovative Techniques for the Implementation of Adaptive Mobile Learning Using the Semantic Web", Ph.D. dissertation, Dept. of Computer Science and Engineering, Univ. of Bridgeport, Bridgeport, CT, 2016.