doi: 10.18178/ijiet.2018.8.10.1125
Ontology Based e-Learning Systems: A Step towards Adaptive Content Recommendation
S. Sarwar1
,
R. García-Castro2 ,
Z. Qayyum3 ,
M. Safyan3 ,
F. Munir4 ,
Muddesar Iqbal5
- 1University of Gujrat, Pakistan
- 2Universidad Politécnica de Madrid (UPM), Spain
- 3University of Gujrat at Department of Computing, Pakistan
- 4Universidad Politécnica de Catalan (UPC), Spain
- 5London South Bank University England, UK
Abstract
E-Learning systems can be made more effective through personalization and adaptivity while recommending the learning content to learners. A comprehensive set of attributes needs to be identified for learner categorization to ensure personalized and adaptive content recommendation. In this paper, a set of core attributes have been identified for effectively profiling the learners and categorizing through neural networks. The learning contents have been annotated formally in ontology for recommending the personalized contents to the learners. Performance of proposed framework is measured in terms of accurate learner categorization, precise recommendation of the learning contents and completeness of ontological model.
Keywords
- Adaptivity
- content recommender e-learning
- personalization
How to Cite
S. Sarwar, R. García-Castro, Z. Qayyum, M. Safyan, F. Munir, and Muddesar Iqbal, "Ontology Based e-Learning Systems: A Step towards Adaptive Content Recommendation," International Journal of Information and Education Technology, vol. 8, no. 10, pp. 700-705, 2018. https://doi.org/10.18178/ijiet.2018.8.10.1125
Copyright & License
Copyright © 2018 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).