Informal Knowledge In E Learning

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These alides were presented during the <a href="http://www.conference.ie/Conferences/index.asp?Conference=38">5th Annual Conference on Teaching & Learning: Learning Technologies</a> in Galway.

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Informal Knowledge In E Learning

  1. 1. Adapting informal sources of knowledge to e-Learning Jacek Jankowski, Jaroslaw Dobrzanski, Filip Czaja Digital Enterprise Research Institute National University of Ireland, Galway <firstname.lastname> @deri.org
  2. 2. Presentation scope <ul><li>Motivation </li></ul><ul><li>Formal and Informal learning </li></ul><ul><li>Didaskon project </li></ul><ul><li>IKHarvester project </li></ul><ul><li>Social Semantic Information Sources (SSIS ) </li></ul><ul><li>Conclusions </li></ul>
  3. 3. Motivation <ul><li>Huge amount of information to capture </li></ul><ul><li>Predefined, rigid courses – made once and for all </li></ul><ul><li>Expensive content creation and maintenance </li></ul><ul><li>80% of possessed knowledge is acquired from informal sources </li></ul>
  4. 4. Formal and Informal learning <ul><li>Formal learning: </li></ul><ul><ul><li>Traditional, old, preparatory approach (i.e. gathering in a classroom) </li></ul></ul><ul><ul><li>Predefined, inflexible courses – made once and for all </li></ul></ul><ul><ul><li>Training is PUSHED </li></ul></ul><ul><ul><li>Employs advanced and expensive solutions (LMS) </li></ul></ul><ul><li>Informal learning: </li></ul><ul><ul><li>More natural, unofficial aproach </li></ul></ul><ul><ul><li>Flexible and spontaneous – learn when/where/what you want </li></ul></ul><ul><ul><li>Learning is PULLED </li></ul></ul><ul><ul><li>Free in most cases </li></ul></ul>
  5. 5. Didaskon <ul><li>Didaskon - a framework for automated composition of a learning path for a student </li></ul><ul><li>Architecture of the future e-Learning system (our idea presented on LACLO 2006): </li></ul><ul><li>Ontology for user model – delivering personalised content </li></ul><ul><li>Ontology for content - ensuring cooperation of heterogeneous environments which use different formats </li></ul>
  6. 6. Didaskon Context
  7. 7. Didaskon - Architecture <ul><li>Didaskon – e-Learning framework, that will be based on existing solutions: </li></ul><ul><li>Users management: FOAFRealm, Windows CardSpace </li></ul><ul><li>Formal Repositories (for learning object’s): LOstRepository </li></ul><ul><li>Informal Repository: IKHarvester </li></ul><ul><li>MarcOnt – handling different formats </li></ul><ul><li>UDDI – Didaskon API description </li></ul>
  8. 8. IKHarvester - Informal Knowledge Harvester
  9. 9. Social Semantic Information Sources (SSIS) <ul><li>Compilation of the Semantic Web and Web 2.0 </li></ul><ul><ul><li>Collaboration </li></ul></ul><ul><ul><li>Sharing </li></ul></ul><ul><ul><li>Semantic annotations for resources </li></ul></ul><ul><ul><li>Interlinking resources and people related to the m </li></ul></ul><ul><ul><li>Dedicated for people and computers </li></ul></ul><ul><li>Examples: </li></ul><ul><ul><li>Semantic wikis: Semantic MediaWiki extension </li></ul></ul><ul><ul><li>Semantic blogs: SIOC Plugin for WordPress </li></ul></ul><ul><ul><li>JeromeDL – the Social Semantic Digital Library </li></ul></ul>
  10. 10. IKHarvester - Goals <ul><li>Capturing informal learning/knowledge from SSIS </li></ul><ul><li>Providing data for eLearning frameworks, e.g. Didaskon </li></ul>
  11. 11. Data Harvesting <ul><li>The Semantic Web </li></ul><ul><ul><li>RDF feeds (semantic wikis) </li></ul></ul><ul><ul><li>Relation with RDF documents </li></ul></ul><ul><ul><ul><li>Information in HTML </li></ul></ul></ul><ul><li>Non-semantic web pages </li></ul><ul><ul><li>HTML of Wikipedia or blogs on Blogger still is quite semantic – common templates of web pages </li></ul></ul><ul><ul><li>HTML scraping </li></ul></ul>
  12. 12. Data Providing <ul><li>Learning Object Metadata (LOM) </li></ul><ul><ul><li>Standard underlying SCORM 2004 </li></ul></ul><ul><li>LOM features: </li></ul><ul><ul><li>Used in a number of LMSs </li></ul></ul><ul><ul><li>Rich description </li></ul></ul><ul><ul><li>Many aspects: educational, technical, relations with other LOs, classification, ... </li></ul></ul>
  13. 13. IKHarvester - Architecture <ul><li>Service Oriented Architecture ensures: </li></ul><ul><ul><li>Encapsulation </li></ul></ul><ul><ul><li>Abstraction – hidden logic </li></ul></ul><ul><ul><li>Loose coupling - independancy </li></ul></ul><ul><ul><li>Quicker reposnses </li></ul></ul><ul><ul><li>Reusability - one deployment, many usages </li></ul></ul><ul><li>REST-based Web Services </li></ul><ul><ul><li>Popular with Web 2.0 and the Semantic Web </li></ul></ul><ul><ul><li>Resource-oriented </li></ul></ul>
  14. 14. IKHarvester – API specification Removes LOM for a specified LO DELETE http://server/ikh/soa/$URI$ Adds/updates LOM for a specified LO PUT / POST http://server/ikh/soa/$URI$ Returns the content of a specified LO GET http://server/ikh/soa/$URI$/content Returns LOM for a specified LO GET http://server/ikh/soa/$URI$/manifest Returns available LOs or LOs of the specified type ( type parameter) GET http://server/ikh/soa/[type] Description HTTP Method URL
  15. 15. Extensibility – support for new types of resources
  16. 16. Comparison with existing tools
  17. 17. Conclusions <ul><li>Features of Didaskon: </li></ul><ul><ul><li>D ynamically build s course s for specific user </li></ul></ul><ul><ul><li>Uses formal courses described in LOM </li></ul></ul><ul><ul><li>Derives from IKHarvester which </li></ul></ul><ul><ul><ul><li>Captures knowledge from informal sources of information ( wikis and blogs ) </li></ul></ul></ul><ul><ul><ul><li>Exposes harvested data in LOM </li></ul></ul></ul>

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