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Six dimensions of Learning Analytics

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Presentation given at Onderwijsdagen 2015, #OWD2015, 09.11.2015, Rotterdam, The Netherlands.

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Six dimensions of Learning Analytics

  1. 1. `` ` Dr. Hendrik Drachsler, UHD, Pre-Con. #OWD2015 09.11.2015 1 Learning Analytics http://www.flickr.com/photos/traftery/4773457853Picture Pic. by Tom Raftery: @HDrachsler
  2. 2. `` ` 3 • Hendrik Drachsler Associate Professor Learning Technologies • Research topics: Personalization, Recommender Systems, Learning Analytics, Mobile devices • Application domains: Schools, HEI, Medical education WhoAmI 2006 - 2009 @HDrachsler
  3. 3. `` ` 3 Research activites SURF
  4. 4. `` ` @HDrachsler, #LASI_NL, Zeist, Netherlands Slide 4 / 29 June 2014 1. Why LA 2. LA Framework Lecture structure 3. Conclusions
  5. 5. `` ` More ICT = More DATA Deze data zijn waardevol en informatief.
  6. 6. `` ` The Big Data Economy Voorbeeld: The Google Flue trend technology … … kun nu ook voor onderwijs toegepast worden. Learning Analytics = Data Science voor educatie
  7. 7. `` ` New MIT study
  8. 8. `` ` Onderwerp via >Beeld >Koptekst en voettekst Pagina 8
  9. 9. `` ` DATA van Learning Profiles DATA van LMS en MOOCs DATA van Learning Resources (Apps, Games,..) DATA van assessments Wat zijn educatief data? De eerste keer dat wij de leerling in het leerproces kunnen volgen. - On Demand Learning Measures -
  10. 10. `` ` 17 Reinhardt, W., Meier, C., Drachsler, H., & Sloep, P. B. (2011). Analyzing 5 years of EC-TEL proceedings. In C. D. Kloos, D. Gillet, R. M. Crespo García, F. Wild, & M. Wolpers (Eds.), Towards Ubiquitous Learning: 6th European Conference of Technology Enhanced Learning, EC- TEL 2011 (pp. 531-536). September, 20-23, 2011, Palermo, Italy. LNCS 6964; Heidelberg, Berlin: Springer. Nieuwe inzichten
  11. 11. `` ` 17 Nieuwe inzichten Dawson, S., Bakharia, A., & Heathcote, E. (2010, May). SNAPP: Realising the affordances of real-time SNA within networked learning environments. In Proceedings of the 7th International Conference on Networked Learning (pp. 125-133). Denmark, Aalborg.
  12. 12. `` ` 17 Graph by Rob Koper. Data science voor de realisatie van online activerend onderwijs. Presentation given at Dag van het Onderwijs (5 November 2015). Heerlen. The Netherlands Nieuwe inzichten Learning Activities Studytime in days
  13. 13. `` ` 17 Graph by Rob Koper. Data science voor de realisatie van online activerend onderwijs. Presentation given at Dag van het Onderwijs (5 November 2015). Heerlen. The Netherlands Nieuwe inzichten Learning Activities Studytime in days
  14. 14. `` ` @HDrachsler, #LASI_NL, Zeist, Netherlands Slide 14 / 29 June 2014 1. Why LA 2. LA Framework 3. Conclusions Lecture structure
  15. 15. `` `
  16. 16. `` ` 16 Greller, W. & Drachsler, H. (2012). Turning Learning into Numbers. Toward a Generic Framework for Learning Analytics. Journal of Educational Technology & Society. http://ifets.info/journals/15_3/4.pdf Visualiz.
  17. 17. `` ` 17 Stakeholders data subjects data clients Greller, W. & Drachsler, H. (2012). Turning Learning into Numbers. Toward a Generic Framework for Learning Analytics. Journal of Educational Technology & Society. http://ifets.info/journals/15_3/4.pdf
  18. 18. `` ` 18 Stakeholders Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist.
  19. 19. `` ` 19 Objectives Reflection Prediction
  20. 20. `` ` 20 Educational Data Drachsler, H., et al. (2010). Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning. 1st Workshop Recommnder Systems in Technology Enhanced Learning (RecSysTEL@EC-TEL 2010) September, 28, 2010, Barcelona, Spain. Verbert, K., Manouselis, N., Drachsler, H., and Duval, E. (2012). Dataset-driven Research to Support Learning and Knowledge Analytics. Journal of Educational Technology & Society. www.ifets.info/journals/15_3/10.pdf
  21. 21. `` ` 21 Edu. Data Storage Berg, A., Scheffel, M., Ternier, S., Drachsler, H., and Specht, M. (submitted). Dutch cooking with xAPI recipes and the flavour of various Learning Record Stores. Learning Analytics and Knowledge conference 2016, Edinburgh, UK. •  Various heterogonous data sources •  No metadata standards •  No proper description of data fields •  No unique user ID in the different systems •  Not intended for evaluation and educational interventions •  No comparison of effective methods
  22. 22. `` ` 22 Edu. Data Storage Berg, A., Scheffel, M., Ternier, S., Drachsler, H., and Specht, M. (submitted). Dutch cooking with xAPI recipes and the flavour of various Learning Record Stores. Learning Analytics and Knowledge conference 2016, Edinburgh, UK.
  23. 23. `` ` Data Standards
  24. 24. `` ` 24 Technologies Prediction Manouselis, N., Drachsler, H., Verbert, K., and Duval, E. (2012). Recommender Systems for Learning. Berlin:Springer
  25. 25. `` ` 25 Technologies Reflection
  26. 26. `` ` 26 Constraints 1. Data security 2. Ethics & Privacy 3. Transparency 4. Ownership
  27. 27. `` ` 27 Constraints •  $100 million investment •  Aim: Personalized learning in public schools, through data & technology standards •  9 US states participated, In 2013 data about millions of children have been stored
  28. 28. `` ` 28 Constraints
  29. 29. `` ` 29 Constraints http://www.open.ac.uk/students/ charter/essential-documents/ ethical-use-student-data-learning- analytics-policy# https://www.jisc.ac.uk/sites/default/files/ jd0040_code_of_practice_for_learning_a nalytics_190515_v1.pdf
  30. 30. `` ` 30 Constraints Engelfriet, A., Jeunink, E., Manderveld, J. (2015). Learning analytics onder de Wet bescherming persoonsgegevens https://www.surf.nl/kennis-en-innovatie/ kennisbank/2015/learning-analytics- onder-de-wet-bescherming- persoonsgegevens.html
  31. 31. `` ` 31 Competences 1. E-literacy 2. Interpretation skills 3. Agency 4. Privacy understanding
  32. 32. `` ` 32 Competences Drachsler, H., Stoyanov, S., d'Aquin, M., Herder, E., Dietze, S., & Guy, M. (2014, 16-19 September). An Evaluation Framework for Data Competitions in TEL. 9th European Conference on Technology-Enhanced Learning (EC-TEL 2014), Graz, Austria.
  33. 33. `` ` @HDrachsler, #LASI_NL, Zeist, Netherlands Slide 33 / 29 June 2014 1. Why LA 2. LA Framework 3. Conclusions Lecture structure
  34. 34. `` ` 34 LA driven instructional Design
  35. 35. `` ` Sophistican model Siemens, G., Dawson, S., & Lynch, G. (2014). Improving the Quality and Productivity of the Higher Education Sector – Policy and Strategy for Systems-Level Deployment of Learning Analytics. Canberra, Australia: Office of Learning and Teaching, Australian Government. Retrieved from http://solaresearch.org/Policy_Strategy_Analytics.pdf LA Sophistication Model
  36. 36. `` ` @HDrachsler, #LASI_NL, Zeist, Netherlands Slide 36 / 29 June 2014 Creative data sourcing, necessary IT support Question- driven, not data or IT driven Participatory design of analytics tools One–size-fits-all does not work in LA and is no innovation Suggestions to do own LA
  37. 37. `` ` Join us: www.laceproject.eu
  38. 38. `` ` 38 This silde is available at: http://www.slideshare.com/Drachsler Email: hendrik.drachsler@ou.nl Skype: celstec-hendrik.drachsler Blogging at: http://www.drachsler.de Twittering at: http://twitter.com/HDrachsler Many thanks for your attention!

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