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Decision Support Systems in E-Learning

Decision Support Systems in E-Learning. Enn Õunapuu Estonia enn@cc.ttu.ee. Content. Outlines Web service Service agent Service oriented architecture Instructional support system architecture Balanced scorecard Decision support component Example Conclusions Questions. Outlines.

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Decision Support Systems in E-Learning

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  1. Decision Support Systems in E-Learning Enn Õunapuu Estonia enn@cc.ttu.ee

  2. Content • Outlines • Web service • Service agent • Service oriented architecture • Instructional support system architecture • Balanced scorecard • Decision support component • Example • Conclusions • Questions

  3. Outlines This presentation describes a solution to the educational systems creation: a architecture and methodology of educational system creation based on web services, service agents and decision support component. In our project we handle the e-learning system as a powerful solution for achieving strategic objectives of the University and measure the impact of the system implementation on the performance of the University both in monetary and non-monetary way.

  4. Web service • Software services are discrete units of application logic that expose message-based interfaces suitable for being accessed across a network. • Interoperability www.ws-i.org • Basic profile standards • SOAP • WSDL • UDDI

  5. Service agent A service agent is a service that helps you work with other services. Often supplied by the provider of the target service, the agent runs topologically close to the application consuming the service. It helps both to prepare requests to a service and to interpret responses from the service.

  6. Service oriented architecture • Service oriented architecture is one where application is cut on the pieces called services • Each service is invoked by messaging • The semantics of the operations are around business functions

  7. IMS An eLearning system architecture

  8. System architecture

  9. Instructional support system architecture

  10. University objectives University should attain the following objectives: • To have outstanding academic personnel; • To perform high-level scientific research work • To establish efficient, innovative and appealing studying/training process; • To have talented students and community support; • To use University resources in most effective and efficient way.

  11. Cause and effect relationships for the University organization

  12. Decision support component Our goal is to find best web service among multiple offerings. Decision support component represent a possible solution for the problem. In this component the multi-criteria analysis methods are implemented. In this approach, we consider service execution statistics, service availability statistics and consumer preferences as input for the multi-criteria analysis techniques.

  13. Multi-criteria analysis • A standard feature of multi-criteria analysis is a performance matrix, or consequence table, in which each row describes an option and each column describes the performance of the options against each criterion. The individual performance assessments are often numerical, but may also be expressed as 'bullet point' scores, or colour coding.

  14. Steps of multi-criteria analysis • Identify the options. • Identify the objectives and criteria that reflect the value associated with the consequences of each option. • Describe the expected performance of each option against the criteria. • Weighting. Assign weights for each of the criteria to reflect their relative importance to the decision. • Combine the weights and scores for each of the options to derive and overall value. • Examine the results.

  15. The performance matrix

  16. Scoring and weighting • Scoring: the expected consequences of each option are assigned a numerical score on a strength of preference scale for each option for each criterion. 0 -100 • Weighting: numerical weights are assigned to define, for each criterion, the relative valuations of a shift between the top and bottom of the chosen scale.

  17. Overall score calculation

  18. Weights

  19. Scored table

  20. Basic AHP procedure At the core of the Analytic Hierarchy Process (AHP) lies a method for converting subjective assessments of relative importance to a set of overall scores or weights.

  21. Scales • Equally important – 1 • Moderately more important – 3 • Strongly more important – 5 • Very strongly more important – 7 • Overwhelmingly more important -9

  22. Calculating the weights

  23. Calculating the weights Saaty's basic method to identify the value of the weights depends on relatively advanced ideas in matrix algebra and calculates the weights as the elements in the eigenvector associated with the maximum eigenvalue of the matrix. For the above set of pairwise comparisons, the resulting weights are: • w1 = 0.751 w2 = 0.178 w3 = 0.070.

  24. Conclusions • Web services and Service agent based interoperability enables the creation of the scalable and flexible global educational systems • To create global educational systems we need a modular suite of specifications that enables enterprises of any size and in any geographical location to conduct interactions over the Internet. • IMS Global learning Consortium offers such specifications • Multi-criteria analysis methods makes possible to make our systems more intelligent

  25. Questions??

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