State and future of linked data in learning analytics


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Presentation at the "Using Linked Data in Learning Analytics" tutorial at LAK 2013.

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State and future of linked data in learning analytics

  1. 1. State of Linked Data in Learning Analytics And future…
  2. 2. Schedule8.30 Intro to the tutorial Linked data and its potential in learning analytics scenarios Basics of manipulating linked data10.30 Coffee break11.00 Using Linked Data in Analytics Tools Evaluation of the Linked Data applications12.30 Lunch13.30 Introduction to the LAK Data challenge Presentations from the LAK Data Challenge particiants15.30 Tea break16.30 Current state of Linked Data in Learning Analytics Results of the challenge Wrap up17.30 Finished
  3. 3. What is (sometimes) being doneLinked data as basic underlying data modellingLinked data as data sourceSemantic Web for ontological models and integrationSome use in recommendationSome use in visualisation / social network analysis
  4. 4. Going further Some other toolSPARQL endpoint SPARQL SPARQL CSV Results proxy Open Refine Excel RDF
  5. 5. Going further Some ! other toolSPARQL endpoint I SPARQL SPARQL N CSV G Results O R proxy B Open Refine Excel RDF
  6. 6. From the LAK Data ChallengeStatistical analysis, network analysisExploration, facet search and browsingRecommendationVisualisation / visual analyticsSearch and retrievalRethorical / narrative analysisTrend Analyis
  7. 7. From LAKLinked data and semantic web in the CFP… butLAK 20131 paper with a strong linked data component1 tutorial (this one)LAK 20121 workshop (LALD 2012)LAK 20111 paper on semantic social analysis 7
  8. 8. Going further mEducator … Gephi Tableau Weka … Results / findings / insights / aggregates / … Interpretation Understanding
  9. 9. ChallengesData: Technology:Overview of what exist Data mining in linked dataIntegration – can we jointly query all of Linked data quality specification these things?Heterogeneity, dealing with multiple Provenance sourcesCoverage, adoption Skills: Development with new technologiesUsage: Dealing with large, distributed dataLinked data for interpretation Change with respect to usual dataLinked data for enrichment management approachesLinked data for re-purposing Moving away from traditionalLinked data for result publication cataloguing approaches
  10. 10. Collecting and cataloguing data
  11. 11. Architecture
  12. 12. Process
  13. 13. CKAN-based catalogue
  14. 14. Data browsing interface
  15. 15. SPARQL endpoint
  16. 16. Vocabularies in the datasets
  17. 17. Types in the datasets
  18. 18. Lightweight-Global integrationSimple manual mapping of the types (classes) in the datasets to a set of selected vocabularies
  19. 19. Supporting development
  20. 20. Education/training eventsLAK 2013 tutorialUsing Linked Data in Learning AnalyticsWWW 2013 tutorialOpen learning and linked dataRio de Janeiro, 14th May 2013SSSW 2013 ( School on Ontology Engineering and the Semantic WebCercedilla (near Madrid), Spain7-13 July 2013Deadline to apply: 12th April 2013
  21. 21. And of course, the Challenge!
  22. 22. Take home message Linked Data is essential to Learning Analytics: provides a flexible, reusable source of information for all the steps of the analytics process Still some efforts to make for a complete understanding by the Learning Analytics community of the benefits of adopting (and learning) linked data But, through various channels, will soon become standard practice 10. April 201323