Using Linked Data in Learning Analytics tutorial - Introduction and basics of manipulating linked data


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Using Linked Data in Learning Analytics tutorial - Introduction and basics of manipulating linked data

  1. 1. Using Linked Data in Learning Analytics LAK 2013 tutorial Mathieu d’Aquin (@mdaquin, (Knowledge Media Institute, The Open University, UK) Stefan Dietze (L3S Research Center, DE) Hendrik Drachsler (CELSTEC, Open Universiteit Nederland, NL) Eelco Herder (L3S Research Center, DE)
  2. 2. Why a Linked Data Tutorial at LAK 2013?A Naïve view Learning Analytics is an application of data analytics on educational data, in learning environment and for the purpose to improve the learning and teaching experience. Linked Data is a set of technologies and principles to expose, publish and interconnect data on the Web. It is very popular nowadays for opendata, eGovernment, academia and the industry because of the flexibility and the global integration possibilities it provides. So, Linked Data used to find, collect and process large amounts of interconnected data to be used in analytics. But It is not only the input! Can be used to complete local data, enrichment them, or for interpretation of the results.
  3. 3. 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
  4. 4. The LAK Data Challenge (preview)Soude Fazeli – Open Universiteit Nederland (Netherlands). Socio-semantic Networks of Research Publications in the Learning Analytics CommunityMichael Derntl, Nikou Günnemann and Ralf Klamma – RWTH Aachen (Germany). A Dynamic Topic Model of Learning Analytics ResearchRicardo Alonso Maturana, María Elena Alvarado, Susana Lopez-Sola, María José Ibáñez and Lorena Ruiz Elósegui – GNOSS (Spain). Linked Data based applications for Learning Analytics Research: faceted searches, enriched contexts, graph browsing and geographic visualisationNikola Milikic, Uros Krcadinac, Jelena Jovanovic, Bojan Brankov and Srdjan Keca – University of Belgrade, UZROK Labs (Serbia).Paperista: Visual Exploration of Semantically Annotated Research PapersSadia Nawaz, Farshid Marbouti and Johannes Strobel – Purdue University (United States). Analysis of the Community of Learning AnalyticsBernardo Pereira Nunes and Besnik Fetahu – L3S Research Center (Germany). Cite4Me: Semantic Retrieval and Analysis of Scientific PublicationsDavide Taibi, Ágnes Sándor, Duygu Simsek, Simon Buckingham Shum, Anna De Liddo and Rebecca Ferguson – Italian National Research Council, Xerox Research Center (France), The Open University (UK). Visualizing the LAK/EDM Literature Using Combined Concept and Rhetorical Sentence ExtractionAmal Zouaq, Srecko Joksimovic and Dragan Gasevic – Royal Military College of Canada, Simon Fraser University, Athabasca University (Canada). Ontology Learning to Analyze Research Trends in Learning Analytics Publications
  5. 5. Your guides to the wild world of linked data Mathieu Stefan Hendrik @mdaquin @stefandietze @hdrachsler Eelco @eelcoherder (put he is not actually here)
  6. 6. Linked data and its potential in learning analytics scenarios
  7. 7. Linked Data Person: Mathieu Open University Website Publication: Pub1 author workFor Open University VLE Course: M366 offers KMi 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. From Linked Data to the Semantic Web rNews Music Ontology Geo Ontology SIOC Media Ontology Dublin Core DBPedia FOAF Ontology DOAP FMA BIBO Ontology LODE Gene Ontology
  9. 9. Example:
  10. 10. information:  580 modules/ description of the course, information about the levels and number of credits associated with it, topics, and conditions of enrolment.Research publications:  16,000 academic articles / information about authors, dates, abstract and venue of the publication.Podcasts:  2220 video podcasts and 1500 audio podcats / short description, topics, link to a representative image and to a transscript if available, information about the course the podcast might relate to and license information regarding the content of the podcast.Open Educational Resources:  640 OpenLearn Units / short description, topics, tags used to annotate the resource, its language, the course it might relate to, and the license that applies to the content.Youtube videos:  900 videos / short description of the video, tags that were used to annotate the video, collection it might be part of and link to the related course if relevant.University buildings:  100 buildings / address, a picture of the building and the sub-divisions of the building into floors and spaces.Library catalogue:  12,000 books/ topics, authors, publisher and ISBN, as well as the course related.Others…
  11. 11. A global data space for education data mEducator The Open University education Research Orgs., ouputs Buidings, Locations Learning resources University of OrganicEduNet Muenster, DE University of University of Bristol Southampton
  12. 12. – Mathieu d‘Aquin 9. April 2013 12
  13. 13. What’s the use in Learning Analytics From Bienkowski, Feng, and Means areas of LA/EDM applications • Modeling user knowledge, behavior, and experience… and connect them to information about the context of learning • Creating profiles of users… that can be interlinked through common objects • Modeling knowledge domains… through online knowledge sources thatData can be numerous and collectively built • Trend analysis… that can beIntegration interpreted through related them to external sources of information Understanding • Personalization and adaptation… using indirect connections to other reference entitiesOriginal image from George Siemens
  14. 14. Example of simple application: Map of OU buildings Interactive map of Open University Buildings in the UK Each dot is a location where buildings can be found. Going over the dot give information about the building there (floors, spaces, car-parks, etc.)
  15. 15. name “Berrill building”bat1 Milton Keynes bat1- address inDistrict inCounty Postcode- mk76aa Buckingha Spaces mshire location Floors Mk76aa- Buildings location ID Address Post- lat long code 52.024924 -0.709726
  16. 16. Simple recommendation: Study at the OU Each topic as a linked data URI. Each course as a linked data URI. Each resource as a linked data URI. They are all connected. Use SPARQL to answer the question: “What are the resources related to this topic or to courses on this topic”
  17. 17. Less simple recommendation: Talis AspireLecturers from different universities put theirreading lists online. Publishing using the principlesof linked data means that all resources areglobally identified, creating a network of resourcesand reading lists.Recommendations can then trivially exploitglobally all these local contributions.
  18. 18. Even less simple recommendation:DiscOU
  19. 19. Resources URIs + Similarity-Based Interface common topics Search BBC Programme or iPlayer page Resource descriptions Indexes Synopsis Named Entity Semantic Semantic Recognition Entities Indexing (Dbpedia) Podcasts, Indexes OpenLearn Units and Semantic Index
  20. 20. Not one scenario:An infinite recombination of data and purposes
  21. 21. Complex analytics in a lightweight way
  22. 22. Analytics across datasetsAcademics in “Arts and Humanities” Topics most commonly mentioned bymost often involved with the media (in news outlets own by the BBC (innumber of news items) number of news items) From the Open University From news clippings From
  23. 23. Complex analytics with rich backgroundinformation
  24. 24. Basics of manipulating linked data
  25. 25. AgendaURIs – the basisRDF – the representation languageOntologies/Vocabularies – for schemas and modelsSPARQL – for queryingSPARQL Update – for modifying (but we won’t say much about this)
  26. 26. URIs – three roles Example: An identifier for a An anchor for linking An access point to data entity Let’s say you have worked representation(s) of there.Here, the Department of Media You – worked-at  this URI the data entityTechnology of the University of In possibly different formats… Aalto, Finland
  27. 27. URI resolving In the browser curl -H "Accept: application/rdf+xml" -L (Accept: text/html) <rdf:Description rdf:about=""> <rdfs:label>RDF description of Department of Media Technology</rdfs:label> <foaf:primaryTopic> <aiiso:Department rdf:about=""> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:code>T3030</aiiso:code> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <foaf:name xml:lang="en">Department of Media Technology</foaf:name> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> 9. April 2013 29 <aiiso:teaches rdf:resource=""/>
  28. 28. RDF – graph data model for linked data and the Web Basic idea: URIs and literals (String, integers) are nodes - connected by links labelled by properties (themselves identified as URIs) rdf:type aiiso:Department ses/noppa/dept_T3030 aiiso:School foaf:name aiiso:part_of rdf:type“Department of Media Technology” “School of Science” urses/noppa/org_SCI foaf:name aiiso:teaches teach:courseTitle rdf:type “Filosofia” aiiso:Course s/noppa/course_Inf-0.1202 dc:language “fi”
  29. 29. RDF+XML<aiiso:Department rdf:about=""> <aiiso:code>T3030</aiiso:code> <foaf:name xml:lang="en">Department of Media Technology</foaf:name> <foaf:name xml:lang="sv">Institutionen för mediateknik</foaf:name> <aiiso:part_of rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/> <aiiso:teaches rdf:resource=""/>
  30. 30. Other syntaxes…… Ntriple, Turtle and JSON-LDSimpler to a certain extent, but same principles
  31. 31. Remember… name “Berrill building” bat1 Milton Keynes bat1- address inDistrict inCounty Postcode- mk76aa Buckingha Spaces mshire location Floors Mk76aa- Buildings location ID Address Post- lat long code 52.024924 -0.709726
  32. 32. Ontologies and VocabulariesRole: Provide common definitions for the types (classes) and properties (relations) used in the RDF representations, and their expected behaviour (meaning)Vocabularies and ontologies we have already seen:• AIISO: Academic Institution Internal Structure Ontology• DC: Dublin Core• FOAF: Friend of a Friend (for people and their connections)• TEACH: For courses and academic programmesUse the same mechanisms as Linked Data:• Classes and properties have URIs• They connect through special properties (rdf:type, rdfs:domain, rdfs:range, rdfs:subClassOf, etc.)Formal ontologies: define more precisely the intended meaning of types and properties based on logical constructs
  33. 33. LinkedUp – Author Name 9. April 2013 35
  34. 34. Example: AIISO aiiso:part_of foaf:Organization rdfs:subClassOf aiiso:responsibleFor rdfs:subClassOf foaf:Agentaiiso:Faculty rdfs:subClassOf rdfs:subClassOf aiiso:teaches aiiso:School aiiso:College rdfs:subClassOf aiiso:responsibleFor aiiso:Department aiiso:KnowledgeGrouping aiiso:Institution rdfs:subClassOf rdfs:subClassOf rdfs:subClassOf aiiso:Course aiiso:Module aiiso:Programme
  35. 35. Example: BIBO bibo:Document bibo:partOf bibo:DocumentPart rdfs:subClassOfrdfs:subClassOf bibo:Book rdfs:subClassOf All bibo:partOf bibo:Article rdfs:subClassOf rdfs:subClassOf bibo:BookSection bibo:EditedBook bibo:AcademicArticle rdfs:subClassOf rdfs:subClassOf bibo:AudioVisualDocument bibo:Chapter <=1 bibo:partOf bibo:Issue <=1 bibo:partOf bibo:Journal
  36. 36. Querying: SPARQLASK query: is this true?ask {<> a aiiso:Department} (is it a department?)ask{<> dc:subject ?x} (is there a subject to this department?)Select query: Get me some dataselect ?org ?name where { ?x a aiiso:Department. ?x aisso:part_of ?org. ?org foaf:name ?name. filter( ?x != <> )} order by ?name limit 100(get the organisations with names that have department, except T3030)Construct query: Build a (sub) RDF graphconstruct {?agent1 foaf:knows ?agent2} where{?agent1 aiiso:responsibleFor ?x. ?agent2:responsibleFor ?x}(Construct of graph of people knowing each-other because of being responsible from the same thing)
  37. 37. Querying: SPARQLSPARQL is also a protocol for Web-based data endpoints…
  38. 38. Exampleselect distinct ?course where { ?course <> <>. ?course a <>}Open University courses available in Nigeria ( /) on
  39. 39. Exampleselect distinct ?q (count(distinct ?t) as ?n) where { ?q a <>. ?q <> ?p. ?p <> ?s. {{?s <> ?c} union {?s <> ?c}}. ?c <> ?t. [] <> ?t.} group by ?q order by desc(?n)How many top level subjects are represented in individual Open University qualifications on
  40. 40. Exampleselect ?broader ?term ?narrowerwhere { graph npgg:subjects { ?subject skos:prefLabel ?term . ?subject skos:broader [ skos:prefLabel ?broader ] . ?_ skos:broader ?subject ; skos:prefLabel ?narrower . } filter(regex(?term, "^Stem cells$", "i"))}order by ?broader ?narrowerBroader and narrower terms for "Stem cells“ on
  41. 41. Exampleselect ?doi ?datawhere { ?doi a npg:Article ; npg:hasDataCitation [ npg:hasLink [ ?_ ?data ] ; npg:type ?type ] . filter(regex(?type, "pdb"))}limit 25Data citations to the Protein Database on
  42. 42. SPARQL updateDelete querydelete {?x ?p ?y} where { ?x a aiiso:Course. ?x ?p ?y. ?a1 aiiso:responsibleFor ?x. ?a2 aiiso:responsibleFor ?x. filter ( ?a1 != ?a2 )}Insert queryinsert {?x a onto:WeirdCourse} where{ ?x a aiiso:Course. ?a1 aiiso:responsibleFor ?x. ?a2 aiiso:responsibleFor ?x. filter ( ?a1 != ?a2 )}
  43. 43. Basic take away message Linked data is about using the web architecture for sharing and connecting data, with some form of meaningful interpretation (semantic web) Potential in Learning Analytics processes: as input data, for data integration, for enrichment, for interpretationBased on simple technologies for web-based access to the data: URI, RDF, Web Schemas, SPARQL
  44. 44. Links and References - http://linkeddatabook.com - http://lucero-project.info - http://linkededucation.org - http://linkedup-challenge.org - - http://uciad.infohttp://, M. and Jay, N. Interpreting Data Mining Results with Linked Data for Learning Analytics: Motivation, Case Study and Direction, LAK 2013, C., d’Aquin M. and Dietze S. (eds) Semantic Web Journal Special Issue on Linked Data for Science and Education.’Aquin M. Linked Data for Open and Distance Learning. Commonwealth of Learnin report., M., Allocca, C. and Collins, T. DiscOU: A Flexible Discovery Engine for Open Educational Resources Using Semantic Indexing and Relationship Summaries, Demo ISWC 2012., M., Feng M. and Means, B. Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief. U.S. Department of Education, Office of Educational Technology