Interpreting Data Mining Results with Linked Data for Learning Analytics


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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

  1. 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. 2. My super naïve view of learninganalytics Insight! Tada! Some kind of data processing VisualisationData (from some educationrelated system)
  3. 3. But actually… Insight! Tadada! Some kind of data processing Visualisation InterpretationData (from some educationrelated system)
  4. 4. Needs more data/information Insight! Tadada Some kind of data dou! processing Visualisation Background InterpretationData (from some knowledge educationrelated system)
  5. 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. 6. What’s linked dataSee the “Using Linked Data in LearningAnalytics” tutorial yesterday
  7. 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. 8. rNews Music Ontology Geo Ontology SIOC Media Ontology Dublin Core DBPedia FOAF OntologyDOAP FMA BIBO Ontology LODE Gene Ontology
  9. 9. Example:
  10. 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. 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. 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. 13. The approach tointerpretation:Building a navigationstructure in thepatterns usingdimensions obtainedin linked data
  14. 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. 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. 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. 17. Exploring the hierarchy
  18. 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. 19. Generalisationof the subjects
  20. 20. Examples • Subjects of booksSubjects ofrelated coursematerial
  21. 21. ExamplesAssessmentmethod
  22. 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. 23. Discussion: It’s a loop Views and Data selection dimensions mining Background InterpretationData (from some knowledge educationrelated system)
  24. 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. 25. Thank you! More info at: @mdaquin http://linkedup-challenge.org tutorial/