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Linked Data Generation for Adaptive Learning Analytics Systems

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The presentation for the paper "Linked Data Generation for Adaptive Learning Analytics Systems" given at the LILE2018 – Learning & Education with Web Data workshop at the WebSci conference 2018 in Amsterdam.

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Linked Data Generation for Adaptive Learning Analytics Systems

  1. 1. 1 Linked Data Generation for Adaptive Learning Analytics Systems Sven Lieber, Ben De Meester, Anastasia Dimou, Ruben Verborgh
  2. 2. 2 Challenges for Adaptive Support Tackle the challenges with Linked Data, but why? How to get Linked Data? Bonus
  3. 3. 3 Challenges for Adaptive Support Tackle the challenges with Linked Data, but why? How to get Linked Data? Bonus
  4. 4. Educational apps challenges and our approach Exercises too easy … goodbye user Adaptive learning based on learning activities across contexts Challenges Approach Exercises too hard … goodbye user
  5. 5. Data for adaptive learning algorithms - example User behavior data Contextual Data How did other Germans with similar dialect perform? Difficulty in Learning activities detected related? Listening Comprehension Task Writing Task
  6. 6. Adaptive learning across contexts - requirements Generic representation of learning activities Express exercise difficulty in learning activity statements Integration with user behavior data Integration across contexts
  7. 7. 7 Challenges for Adaptive Support Tackle the challenges with Linked Data, but why? How to get Linked Data? Bonus
  8. 8. How are learning activity statements generated? 8 Learning Activities Learners Learning Record Stores Supporting xAPI (former TinCan)
  9. 9. Why Linked Data? 9 { “Actor”: { “mbox”: “Sven@example.com” }, “Verb”: { “id”: “attempted” }, “Object”: { “objectType”: “Activity”, “id”: “DutchListeningComprehension”, “extensions”: { ..} }, “Context”: { “tookPlaceIn”: “Classroom” } } “extensions”: { “educationalDifficulty”: “easy”, “Type”: “MultipleChoice”, “Options”: { ... } “maxTimeAllowed”: “PT10S” } Learning Activity expressed as Experience API (xAPI) statement Extension for exercise difficulty Generic representation Exercise Difficulty Integration with user behavior data Integration across contexts
  10. 10. What is Linked Data? 10 ex:AuditoryDiscrimination ex:UserAbility ex:SvenListeningMastery ex:Mastery rdf:typ e “0.6” ex:DutchListening Comprehension1 ex:Sven “Sven@example.com” ex:classroom ex:German xapi:hasVerbxapi:Learning Statement1 xapi:attempted ex:Swabian
  11. 11. Quick recap 11 Generic representation of learning activities (using xAPI) Express exercise difficulty in learning activity statements (using xAPI extension) Integration with user behavior data (using Linked Data) Integration across exercises (using Linked Data)
  12. 12. 12 Challenges for Adaptive Support Tackle the challenges with Linked Data, but why? How to get Linked Data? Bonus
  13. 13. How do we generate Linked Data? 13 Learning Record Store Extract Transform Load Files RDF store Linked Data Mapping Load... JSON-LD contexts Extract “Actor”: { “mailbox”: “sven@example.com” } @prefix ex: <http://example.com/> . @prefix foaf: <http://xmlns.com/foaf/spec/> . @prefix xapi: <http://semweb.mmlab.be/ns/tincan2prov/> . ex:sven a xapi:Actor . ex:sven foaf:mbox “sven@example.com” .
  14. 14. 14 Take-home message With Linked Data we achieve an integration of user behavioral data with learning activities and also have an integration across contexts, which improves the learning experience
  15. 15. 15 Challenges for Adaptive Support Tackle the challenges with Linked Data, but why? How to get Linked Data? Bonus
  16. 16. Challenges ahead! 16 Information European General Data Protection Regulation (GDPR) Rights Challenge: Search & Find personal data Rectification Deletion Data Portability
  17. 17. Collect provenance 17 Learning Record Store Extract Transform Load Files RDF store Linked Data Mapping Load... JSON-LD contexts Extract Provenance Provenance Provenance...
  18. 18. Which provenance is collected? 18 ex:xAPIExtraction-v1 statements.json “1353465326” “1353465799” gdprov:DataCollection “xAPI Extraction” endTime ex:LinkedDataTransformation- v1 ex:TinCan2PROV- JSON-LDContext “1353565568” “1353665777” gdprov:DataTransformation “Linked Data Transformation” endTime previousStep
  19. 19. 19 Take-home message With Linked Data we achieve an integration of user behavioral data with learning activities and also have an integration across contexts, which improves the learning experience + more granular control through Linked Data (GDPR, Provenance)
  20. 20. Sven Lieber Researcher Semantic Web Sven.Lieber@ugent.be +32 9 331 49 59 https://sven-lieber.org @SvenLieber Icons from Noun Project Carlos sarmento, Gregor Cresnar, Paperclip
  21. 21. Backup Slide JSON-LD 21 { “@context”: { “tincan2prov”: “http://semweb.mmlab.be/ns/tincan2prov/”, “Actor” : { “@id” : “tincan2prov:Actor” }, … YOUR JSON … } }
  22. 22. Backup Slide RDF (turtle format) 22

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