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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

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

    • 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.net - @mdaquin mathieu.daquin@open.ac.uk Nicolas Jay Université de Lorraine, LORIA, nicolas.jay@loria.fr
    • My super naïve view of learninganalytics Insight! Tada! Some kind of data processing VisualisationData (from some educationrelated system)
    • But actually… Insight! Tadada! Some kind of data processing Visualisation InterpretationData (from some educationrelated system)
    • Needs more data/information Insight! Tadada Some kind of data dou! processing Visualisation Background InterpretationData (from some knowledge educationrelated system)
    • 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
    • What’s linked dataSee the “Using Linked Data in LearningAnalytics” tutorial yesterdayhttp://linkedu.eu/event/lak2013-linkeddata-tutorial/
    • 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
    • rNews Music Ontology Geo Ontology SIOC Media Ontology Dublin Core DBPedia FOAF OntologyDOAP FMA BIBO Ontology LODE Gene Ontology
    • Example: data.open.ac.uk
    • 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
    • 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.
    • 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.)
    • The approach tointerpretation:Building a navigationstructure in thepatterns usingdimensions obtainedin linked data
    • Making the results linked datacompliantUse a simple ontology of sequences to representthe patternsAnd use linked data URIs to represent the items,e.g. DSE212 http://data.open.ac.uk/course/dse212
    • 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.
    • 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
    • Exploring the hierarchy
    • 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
    • Generalisationof the subjects
    • Examples • Subjects of booksSubjects ofrelated coursematerial
    • ExamplesAssessmentmethod
    • 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) http://data.linkededucation.org/linkedup/catalog
    • Discussion: It’s a loop Views and Data selection dimensions mining Background InterpretationData (from some knowledge educationrelated system)
    • 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
    • Thank you! More info at: http://mdaquin.net @mdaquin http://linkedup-project.eu http://linkedup-challenge.orghttp://linkedu.eu/event/lak2013-linkeddata- tutorial/