New e-Learning system architecture based on knowledge engineering technology
Conference paper
Li, Y., Chen, Z., Huang, R. and Cheng, X. 2009. New e-Learning system architecture based on knowledge engineering technology. 2009 IEEE International Conference on Systems, Man and Cybernetics. San Antonio, TX, USA 11 - 14 Oct 2009 IEEE. pp. 5140-5144 https://doi.org/10.1109/ICSMC.2009.5346013
| Type | Conference paper |
|---|---|
| Title | New e-Learning system architecture based on knowledge engineering technology |
| Authors | Li, Y., Chen, Z., Huang, R. and Cheng, X. |
| Abstract | The paper focuses on the field of research on next generational e-Learning facility, in which knowledge-enhanced systems are the most important candidates. In the paper, a reference architecture based on the technologies of knowledge engineering is proposed, which has following three intrinsic characteristics, first, education ontologies are used to facilitate the integration of static learning resource and dynamic learning resource, second, based on semantic-enriched relationships between Learning Objects (LOs), it provides more advanced features for sharing, reusing and repurposing of LOs, third, with the concept of knowledge object, which is extended from LO, an implementing mechanism for knowledge extraction and knowledge evolution in e-Learning facilities is provided. With this reference architecture, a prototype system called FekLoma (Flexible Extensive Knowledge Learning Object Management Architecture) has been realized, and testing on it is carrying out. |
| Keywords | Ontologies; Learning Object; Knowledge Object; Knowledge engineering |
| Research Group | Artificial Intelligence group |
| Conference | 2009 IEEE International Conference on Systems, Man and Cybernetics |
| Page range | 5140-5144 |
| Proceedings Title | 2009 IEEE International Conference on Systems, Man and Cybernetics |
| Series | IEEE International Conference on Systems Man and Cybernetics Conference Proceedings |
| ISSN | 1062-922X |
| ISBN | |
| Hardcover | 9781424427932 |
| Publisher | IEEE |
| Publication dates | |
| Oct 2009 | |
| Online | 04 Dec 2009 |
| Publication process dates | |
| Deposited | 26 Apr 2011 |
| Output status | Published |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/ICSMC.2009.5346013 |
| Web of Science identifier | WOS:000279574602282 |
| Web address (URL) of conference proceedings | https://ieeexplore.ieee.org/xpl/conhome/5340904/proceeding |
| Language | English |
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Guo, X., Huang, Y., Ye, J., Yin, S., Li, M., Li, Z., Yiu, S. and Cheng, X. 2021. ShadowFPE: new encrypted web application solution based on shadow DOM. Mobile Networks and Applications. 26 (4), pp. 1733-1746. https://doi.org/10.1007/s11036-019-01509-yLearning context-aware outfit recommendation
Abugabah, A., Cheng, X. and Wang, J. 2020. Learning context-aware outfit recommendation. Symmetry. 12 (6), pp. 1-13. https://doi.org/10.3390/sym12060873