Learning Analytics and Linked Data Workshop at LAK12

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Introduction slides for the LALD workshop

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Learning Analytics and Linked Data Workshop at LAK12

  1. 1. 29th April 2012Hendrik Drachsler CELSTEC NL | Abelardo Pardo University of Madrid ESStefan Dietze L3S, DE #LALD Wolfgang ReinhardtMathieu d’Aquin The Open University UK University of Paderborn, DEWolfgang Greller Open University NL Katrien VerbertJelena Jovanovic, University of Belgrade, SR K.U. Leuven, BE 1
  2. 2. Overview Motivation dataTEL Linked Education LALD 2
  3. 3. Motivation for #LALD dataTEL LinkedEdu Collecting datasets Connecting datasetsRelease datasets publicly Make datasets accessible Great BIG datasets Great BIG datasets http://bit.ly/datatel http://bit.ly/LinkEdu
  4. 4. Web LinkedUp submissi Motivationdata on data( Personal( data Stage(1JInitialisationInitialisation 36stages6of6the6LinkedUp competition6 LinkedUp Challenge6Environment • Lowest(requirements(level(for(participation • Inital(prototypes(and(mockups,(use(of(data( • LinkedUp Evaluation(Framework Participation criteria testbed(required • Methods and Test(Cases Stage(2 • 10(to(20(projects(are(expected • LinkedUp Data(Testbed • Competitor ranking list • Medium(requirements(level(for(participation • Working(prototypes,(minimum(amount of data(sources,(clear(target(user(group LinkedUp Support Actions Stage(3 • 5(to(10(projects(are(expected( • Dissemination((events,( training)( • Data(sharing(initiatives • Deployment(in(realJworld( use(cases • Community(building(&(clustering • Sustainable(technologies,(reaching out • Technology(transfer to critical(amount(of(users, Stage(4 • 3(to(5(projects(are(expected( • Cashprice( awards(&(consulting E P S T P FNetwork(of(supporting(organisations( ( I(see 3.2Spreadingexcellence,exploitingresults,disseminatingknowledge) S E C B O C ! 4
  5. 5. #LALD main objectives ...... to connect the research efforts on LinkedData and Learning Analytics ...... to create visionary ideas how to combinethe Web of Data and Learning Analytics ...... to support TEL processes andapplications. 5
  6. 6. #LALDAgenda 6
  7. 7. Plus-Minus-Interesting RatingListen to the presentations of the LALD workshopMeanwhile…create notes and/or tweetsP: PlusM: MinusI: Interestinge.g., #LALD #PlusWrite down everything that comes to your mind, generate as many ideas as possible, do not filter your ideas. 7
  8. 8. Overview Motivation dataTEL Linked Education LALD 8
  9. 9. Who is dataTEL ? dataTEL was a Theme Team funded by the STELLAR network of excellence (2009 - 2010) Riina Stephanie Katrien Nikos Martin HendrikVuorikari Lindstaedt Verbert Manouselis Wolpers Drachsler 9
  10. 10. Who is dataTEL ? dataTEL was a Theme Team funded by the STELLAR network of excellence (2009 - 2010) Riina Stephanie Katrien Nikos Martin Hendrik Vuorikari Lindstaedt Verbert Manouselis Wolpers Drachsler MAVSEL CEN PT Social Data Miguel Joris AbelardoAngel Sicillia Klerkx 9 Pardo
  11. 11. Who is dataTEL ? Riina Stephanie Katrien Nikos Martin HendrikVuorikari Lindstaedt Verbert Manouselis Wolpers Drachsler MAVSEL CEN PT Social Data Miguel JorisAngel Sicillia Klerkx10
  12. 12. Data-driven Research and Learning Analytics EATEL-Hendrik Drachsler (a), Katrien Verbert (b)(a) CELSTEC, Open University of the Netherlands(b) Dept. Computer Science, K.U.Leuven, Belgium 11
  13. 13. Objectives SIG dataTEL• Fostering of a research network on educational datasets• Representing dataTEL researchers to promote the release of open datasets• Contributing to policies on ethical implications (privacy and legal protection rights)• Fostering a shared understanding of evaluation methods in Learning Analytics• Fostering the standardizations of datasets to enable exchange and interoperability 12
  14. 14. Survey on TEL RecommenderManouselis, N., Drachsler, H., Vuorikari, R., Hummel, H. G. K., & Koper, R. (2011).Recommender Systems in Technology Enhanced Learning. In P. B. Kantor, F. Ricci, L.Rokach, & B. Shapira (Eds.), Recommender Systems Handbook (pp. 387-415).Berlin: Springer. 13
  15. 15. Survey on TEL RecommenderManouselis, N., Drachsler, H., Vuorikari, R., Hummel, H. G. K., & Koper, R. (2011).Recommender Systems in Technology Enhanced Learning. In P. B. Kantor, F. Ricci, L.Rokach, & B. Shapira (Eds.), Recommender Systems Handbook (pp. 387-415).Berlin: Springer. 13
  16. 16. Survey on TEL Recommender Conclusions: Half of the systems (11/20) still at design or prototyping stage only 9 systems evaluated through trials with human users.Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H. G. K., & Koper, R. (2011).Recommender Systems in Technology Enhanced Learning. In P. B. Kantor, F. Ricci, L.Rokach, & B. Shapira (Eds.), Recommender Systems Handbook (pp. 387-415).Berlin: Springer. 13
  17. 17. The TEL recommenderresearch is a bit like this... 14
  18. 18. But...“The performance resultsof different researchefforts in TELrecommender systemsare hardly comparable.”(Manouselis et al., 2010) Kaptain Kobold http://www.flickr.com/photos/ kaptainkobold/3203311346/ 15
  19. 19. But...“The performance resultsThe TEL recommenderof different researchexperiments lackefforts in TELtransparency. They needrecommender systemsto be repeatable to test:are hardly comparable.”• Validity(Manouselis et al., 2010)• Verification• Compare results Kaptain Kobold http://www.flickr.com/photos/ kaptainkobold/3203311346/ 15
  20. 20. Drachsler, H., Bogers, T., Vuorikari, R., Verbert, K., Duval, E., Manouselis, N., Beham, G.,Lindstaedt, S., Stern, H., Friedrich, M., & Wolpers, M. (2010). Issues and Considerationsregarding Sharable Data Sets for Recommender Systems in Technology Enhanced LearningElsevier Procedia Computer, Science, 1, 2, pp. 2849 - 2858.
  21. 21. dataTEL::Collection 17
  22. 22. dataTEL::ContextManouselis, N., Drachsler, H., Verbert, K., Duval, E. (2012). Recommender Systems for Learning. Berlin: Springer. 18
  23. 23. Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., Beham, G., Duval, E.,(2011). Dataset-driven Research for Improving Recommender Systems for Learning. LearningAnalytics & Knowledge: February 27-March 1,19 2011, Banff, Alberta, Canada
  24. 24. Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-driven Research toSupport Learning and Knowledge Analytics. (Eds.) Siemens, George and Gašević, Dragan.Educational Technology & Society 20
  25. 25. Drachsler, H., Verbert, K., Manouselis, N., Wolpers, M., Vuorikari, R., Lindstaedt, S. (to appear). Special Issue on Data-Supported Technology-Enhanced Learning at International Journal for TEL.21
  26. 26. Grand Challenges 1. Topic: Evaluation of recommender systems in TEL 2. Topic: Data supported learning examples 3. Topic: Datasets from learning object repositories and web content 4. Topic: Privacy and data protection for educational datasets Drachsler, H., Verbert, K., Manouselis, N., Wolpers, M., Vuorikari, R., Lindstaedt, S. (to appear). Special Issue on Data-Supported Technology-Enhanced Learning at International Journal for TEL.21
  27. 27. 10 years of TEL RecSys research in one BOOK Chapter 1: Background Chapter 2: TEL context Recommender Chapter 3: Extended survey Systems for of 42 RecSys Learning Chapter 4: Challenges and OutlookManouselis, N., Drachsler, H., Verbert, K., Duval, E.(2012). Recommender Systems for Learning. Berlin:Springer. 22
  28. 28. 10 years of TEL RecSys research in one BOOK Chapter 1: Background Chapter 2: TEL context Recommender Chapter 3: Extended survey Systems for of 42 RecSys Learning Chapter 4: Challenges and OutlookManouselis, N., Drachsler, H., Verbert, K., Duval, E.(2012). Recommender Systems for Learning. Berlin:Springer. http://bit.ly/RecSys 22
  29. 29. Overview Motivation dataTEL Linked Education LALD 23
  30. 30. State of the ArtEducational Resources/Data on the WebState§ Vast Open Educational Resource (OER) metadata collections (OpenCourseware, ARIADNE, OpenLearn)§ University channels on YouTube and iTunes§ Data on courses, teachers, institutions§ Competing Web interfaces (e.g. SQI, OAI-PMH, SOAP),§ Competing metadata standards (e.g. IEEE LOM, ADL SCORM, DC…)§ Competing exchange formats (e.g. JSON, RDF, XML)http://purl.org/dietze Educational Web Data & Resources for (Informal) Learning
  31. 31. State of the ArtEducational Resources/Data on the WebIssues§ Heterogeneity & lack of interoperability§ Lack of take-up (c) Paul Millerhttp://purl.org/dietze Educational Web Data & Resources for (Informal) Learning
  32. 32. (c) Paul Miller State of the Art Web of Linked Data(c) Paul Miller
  33. 33. (c) Paul MillerState of the ArtWeb of Linked DataLinked (Open) Data§Vision: well connected graphof open Web data§W3C standards (RDF,SPARQL) to expose data,URIs to interlink datasets§=> vast cloud ofinterconnected datasets(currently over 300 datasets,30+ billions of triples)§Crossing all sorts of domainshttp://purl.org/dietze Educational Web Data & Resources for (Informal) Learning
  34. 34. State of the ArtWeb of Linked Data for EducationDatasets which might enhance (informal) learning§ Publications & literature § ACM, PubMed, DBLP (L3S)… § OpenLibrary…§ Domain-specific knowledge & resources § Bioportal for Life Sciences § Historic artefacts in Europeana § Geonames for geodata § …§ Cross-domain knowledge § DBpedia, Freebase, …§ Media resource metadata § BBC, Flickr, …http://purl.org/dietze Educational Web Data & Resources for (Informal) Learning
  35. 35. State of the ArtWeb of Linked Data for EducationUniversity Linked Data: => for details, see also: http:// linkededucation.org & http://§ The Open University (UK): http://data.open.ac.uk linkeduniversities.org§ CNR (IT): http://data.cnr.it,§ Southampton University (UK): http://data.southampton.ac.uk/§ University of Munster (DE): www.lodum.de§ http://education.data.gov.uk§ …and many more….Open Educational Resources Linked Data:§ mEducator Linked Educational Resources (http://ckan.net/package/meducator)§ Open Learn LD§ ARIADNE RDF§ ..and many more….http://purl.org/dietze Educational Web Data & Resources for (Informal) Learning
  36. 36. State of the Art Web of Linked Data for Education: Applications Some LOD uses (eg from LILE2012): § Web-wide search of educational courses/ OER (educational graph) § Game-based learning & automatic generation of assessment items from LOD facts § Enrichment of learning resources (facilitating more exploratory learning approaches)….http://metamorphosis.med.duth.gr/ http://purl.org/dietze Educational Web Data & Resources for (Informal) Learning
  37. 37. Educational perspective on Web data technologiesSome observations & questionsObservations§Amount of relevant data increasing rapidly,§But educational applications are missing which make large-scale use ofopen Web data (like in many other domains…)Educational perspective on questions like§How can learners/educators benefit from the wealth of relevant data?(enriching learning content/experience, informal learning, generating OER...)§What kinds of data are able to aid or enhance (informal) learning?(OER metadata, domain/cross-domain knowledge, educational vocabularies, egof competencies)§What are requirements from an educators perspective on educationaldatasets?§How can (often poorly structured & heterogeneous) OER metadata qualitybenefit from LOD to ease finding of learning resources? (disambiguation,expansion, clustering)http://purl.org/dietze Educational Web Data & Resources for (Informal) Learning
  38. 38. Overview Motivation dataTEL Linked Education LALD 32
  39. 39. LinkedData might provide...• BIG datasets• Standards to educational datasets• A collection of reference datasets for TEL• your contribution... 33
  40. 40. #LALDAgenda 34
  41. 41. Grand Challenge Structure(a) Grand Challenge description(b) Needed actions to overcome the Grand Challenge(c) Timeframe for the Grand Challenge to overcome(d) Measurable progress and success indicators(e) Possible funding opportunities 35
  42. 42. Grand Challenge ExampleA generic framework to share, analyse, and reuse educational datasets(a) Grand Challenge descriptionThe increased application of LMS, e-portfolios, and PLEs inschools and higher education institutions produces largeamounts of educational data. But, although these e-learningenvironments store educational data automatically, exploitationof this data for new learning services and advanced research onthe phenomena “learning” is still very limited. Thus, there is anunused opportunity for the evaluation of learning theories, thedevelopment of future learning applications, and the evaluationof didactical concepts and educational interventions. A genericframework to... 36
  43. 43. Grand Challenge ExampleA generic framework to share, analyse, and reuse educational datasets(c) Timeframe for the Grand Challenge Problem5 and to 8 years will be needed to overcome the currentsituation and achieve more sharable datasets ...(d) Measurable progress and success indicators • An increasing amount of publicly available datasets and research articles that are based on shared datasets • The availability of data or privacy policies at educational providers • More data-driven tools at educational providers • A common dataset format 37
  44. 44. Grand Challenge ExampleA generic framework to share, analyse, and reuse educational datasets(b) Needed actions to overcome the GC 1. Data ownership and access rights are challenging because ... 2. Data policies (licences) that regulate how different users can use, share, and... 3. There is a lack of common dataset formats like suggested from the CEN PT Social Data group... 4. Standardised methods are needed to anonymise and pre- process educational data according to privacy ... 38
  45. 45. Many thanks for your attention, and now let us contribute to the state of the art... 39picture by Tom Raftery http://www.flickr.com/photos/traftery/4773457853/sizes/l
  46. 46. Many thanks for your attention, and now let us contribute to the state of the art... Free the data 39picture by Tom Raftery http://www.flickr.com/photos/traftery/4773457853/sizes/l

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