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Personalized resource sequencing in digital libraries

Sergio Gutiérrez, Abelardo Pardo, Carlos Delgado Kloos Department of Telematic Engineering Carlos III University of Madrid, Spain gradient.it.uc3m.es. Personalized resource sequencing in digital libraries. Access at no cost. Before How do I have access to a resource? Now

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Personalized resource sequencing in digital libraries

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  1. Sergio Gutiérrez, Abelardo Pardo, Carlos Delgado Kloos Department of Telematic Engineering Carlos III University of Madrid, Spain gradient.it.uc3m.es Personalized resource sequencing in digital libraries

  2. Access at no cost Before How do I have access to a resource? Now How do I select the proper resource?

  3. Consequences • Prepare learning material in a different manner • Re-search with different strategies • New tools • New methodologies

  4. Effective filtering • “Searching” is no longer effective. • Additional filtering is required • User profiles need to be considered to improve effectiveness Adaptation!

  5. Adaptation • Well known research area • Two strategies • Adapt the learning content • Adapt the sequence of resources offered to the learner

  6. Capture relevant resource sequences • Resources are related. • Sequences of resources are captured. • Sequences are dynamically updated

  7. Hierarchical transition graphs • Graphs capture relations between nodes. • Sequences are intuitively defined. • Difficult to handle a large number of nodes.

  8. Graph Definition • Nodes represent resources • Edges represent a transition to another resource • Conditions “enable” the transitions

  9. Conditions/Actions • Condition is stated in terms of any environment variable • If condition holds true, resource is visible • If resource is selected, actions are executed • Resources may use the environment

  10. Hierarchical organization • Graphs with large number of nodes are not feasible • Hierarchy is already present in resources

  11. Example of hierarchy • Transition structures defined between closely related resources. • Transitions among related topics

  12. How is the graph created? • Initial structure derived from relevance • Built-in adaptive structures • Dynamic changes derived from observations

  13. Capturing relevance • Derive graph from high level content organization • Include possibly related items

  14. Built-in adaptive structures • If learner visits more than n resources, offer deeper content • If learner barely uses a resource, force alternatives

  15. Dynamic changes in the graph • Observe behavior of a set of learners. • Give higher priority to “most useful” sequences. • Learner valuation of resources. • Ant colony optimization strategies.

  16. Conclusions • Need “intelligent” resource management • Adapt sequences • Hierarchical graphs to capture sequences • Static and dynamic graph update

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