Interpreting Data Mining Results with Linked Data for Learning Analytics
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Interpreting Data Mining Results with Linked Data for Learning Analytics



Interpreting Data Mining Results with Linked Data for Learning Analytics: Motivation, Case Study and Directions

Interpreting Data Mining Results with Linked Data for Learning Analytics: Motivation, Case Study and Directions
Presentation at the LAK 2013 conference - 10-04-2013



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Interpreting Data Mining Results with Linked Data for Learning Analytics Presentation Transcript

  • 1. Interpreting Data Mining Results with Linked Data for Learning Analytics:Motivation, Case Study and Directions Mathieu d’Aquin Knowledge Media Institute, The Open University - @mdaquin Nicolas Jay Université de Lorraine, LORIA,
  • 2. My super naïve view of learninganalytics Insight! Tada! Some kind of data processing VisualisationData (from some educationrelated system)
  • 3. But actually… Insight! Tadada! Some kind of data processing Visualisation InterpretationData (from some educationrelated system)
  • 4. Needs more data/information Insight! Tadada Some kind of data dou! processing Visualisation Background InterpretationData (from some knowledge educationrelated system)
  • 5. The challenge for learning analyticsMost of the time, background knowledgeneeds to be in the head of the people lookingat the analytics.How to find/obtain background informationfor interpretation to support him/herconsidering that: – The data we are analysing and insight we are trying to obtain can cover a wide range of things, topics, domains, subjects… – We might not know in advance we background information is needed for interpretation Our approach: Integrate linked datasources at the time of interpretation
  • 6. What’s linked dataSee the “Using Linked Data in LearningAnalytics” tutorial yesterday
  • 7. Linked Data Open University Person: Mathieu Website Publication: Pub1 author workFor Open University VLE Course: M366 offersKMi Website M366 Course page Organisation: The Open University Mathieu’s Homepage availableIn setBook Mathieu’s List of Mathieu’s Publications Twitter Country: Belgium Book: Mechatronics The Web The Web of Linked Data
  • 8. rNews Music Ontology Geo Ontology SIOC Media Ontology Dublin Core DBPedia FOAF OntologyDOAP FMA BIBO Ontology LODE Gene Ontology
  • 9. Example:
  • 10. Use case: student enrolment dataFrom theOpenUniversity’sCourse ProfileFacebookApplication: Examples:Who enrolledto what Student ID Course Code Status Date 112 dse212 Studying 2007course at 112 d315 Intend to study 2008what time 109 a207 Completed 2005
  • 11. Sequence miningWe can represent each student’s trajectory by asequence of courses, e.g. (DD100)  (D203, S180)  (S283)Applying sequence mining makes it possible tofind frequent patterns in these sequences, i.e.,courses often taken together in a certain order.
  • 12. The results(and again, why they need background knowledge forinterpretation)Out of 8,806 sequences (students), we obtained126 different sequential patterns with a supportthreshold of 100*i.e. filtering out patterns included in less than 100 sequences. Sequential pattern Support (DD100)  (DSE212) 232 Examples: (DSE212)  (ED209)  (DD303) 150 (B120)  (B201) 122How to know what that means?We need background information about thecourses (DD100, DSE212, ED209 ,etc.)
  • 13. The approach tointerpretation:Building a navigationstructure in thepatterns usingdimensions obtainedin linked data
  • 14. Making the results linked datacompliantUse a simple ontology of sequences to representthe patternsAnd use linked data URIs to represent the items,e.g. DSE212 
  • 15. Selecting a dimension in linked dataPropose relations thatapply to the items ofthe patternsThen relations thatapply to the objects ofthese relationsEtc.i.e. follow the links to build a chain ofrelationships.
  • 16. Building a hierarchy of patternsThe end-values of thechain of relations builtout of following linksof linked data formattributes of thepatternsBuild a lattice(hierarchy) ofconcepts representinggroupings of theseattributes, usingformal conceptanalysis
  • 17. Exploring the hierarchy
  • 18. Benefits(see following examples)Provides an overview of the patternsobtained along a custom dimensionHelps identifying gaps and issues inthe original data/processHelps identifying areas in need offurther explorationGeneric: can be straightforwardlyapplied to other source data, otherlinked data and other mining methods
  • 19. Generalisationof the subjects
  • 20. Examples • Subjects of booksSubjects ofrelated coursematerial
  • 21. ExamplesAssessmentmethod
  • 22. DiscussionLimitations of the approach: – Requires the results to be linked data and the items to connect to linked data – Sources of linked data needs to be available to support interpretation)
  • 23. Discussion: It’s a loop Views and Data selection dimensions mining Background InterpretationData (from some knowledge educationrelated system)
  • 24. ConclusionLinked data can be used to enrich andbring some meaningful structure tothe patterns from an analytics/miningprocessIntroducing linked data not only ininput of the process, but also insupport of more analytical tasksPromising, considering the growth ofeducation-related linked dataShould become part of an iterativeprocess, where patterns and data getrefined through interpretation and theintroduction of backgroundinformation from linked data
  • 25. Thank you! More info at: @mdaquin http://linkedup-challenge.org tutorial/