RESOURCE MANAGEMENT FOR WEB-BASED E-LEARNING

Recent advances in Web and information technologies have resulted in many e-learning resources. There is an emerging requirement to manage and reuse relevant resources together to achieve on-demand e-learning in the Web. We argue that to meet the requirements of resource management for Web-based e-learning. In this paper, we provide a semantic mapping mechanism to integrate e-learning databases by using ontology semantics. Heterogeneous e-learning databases can be integrated under a mediated ontology. Taking into account the locality of resource reuse, we propose to represent context specific portions from the whole ontology as sub ontologies. We present a sub ontology-based approach for resource reuse by using an evolutionary algorithm. We also conduct simulation experiments to evaluate the approach with a traditional Chinese medicine e-learning scenario and obtain promising results.

Existing System:

Existing works for Semantic-Web- or ontology-based E-learning tend to use ontologies or semantic models Statically to mediate e-learning resources or improve E-learning behaviors. it is necessary for users to Retrieve and reuse them in a global scope. An e-learning System needs to compose relevant resources together in Order to achieve on-demand and collaborative e-learning in The Web. However, there exists the heterogeneous representation Problem to various e-learning resources in the Web.

Proposed System:

In contrast to the above and the approaches reviewed earlier, our work on e-learning resource management relies on a SubO-based approach that reuses large-scale ontology dynamically. The way we integrate e-learning resource by semantic mapping is similar with existing research on ontology-based mapping or integration of e-learning resources; however, we have extended the approach with a dynamic SubO evolution mechanism for resource reuse. To contrast it with ontology modularity and ontology evolution, our concern of SubO evolution is inclined to evolve the resource repository of the e-learning system based on GA.

Algorithm Used:

Sub 0 Evolutions:

§  Extract

§  Encode

§  Population evaluation

§  Decode

§  Store

§  Compare

§  Retrieve

Techniques Used:

ü  Dynamics Resource Reuse

Concept:

§  Structure Search

§  Unstructured Search

Requirements:

Hardware Requirement:-

                       Hard Disk                    -           20 GB

                       Monitor                       -           15’ Color with VGI card support

                       RAM                            -           Minimum 256 MB

                       Processor                    -           Pentium III and Above (or) Equivalent

                       Processor speed         -           Minimum 500 MHz

            Software Requirement:-

                       Operating System      -           Windows XP

                       Platform                      -           Visual Studio .Net 2005

                       Database                     -           SQL Server 2000

                       Languages                   -           Asp.Net, C#.Net