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Learning Analytics - Sweden


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This presentation was given by Peter Karlberg of the National Agency for Education (Skolverket) of Sweden at the GCES Conference on Education Governance: The Role of Data in Tallinn on 13 February during the afternoon session workshop on Learning Analytics.

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Learning Analytics - Sweden

  1. 1. Education governance; The role of data Workshop C: Learning Analytics Peter Karlberg | Skolverket twitter: peterkarlberg linkedin: #laceproject
  2. 2. What is learning analytics? • the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs – First International Conference on Learning Analytics And Knowledge (LAK11), Banff, Alberta, Feb 27-Mar 1, 2011 – other terms; Educational data mining (EDM), Big data in education, learner analytics …..
  3. 3. Photo public domain: - Erik Duval analytics-and-educational-data-mining/ “collecting traces that learners leave behind and using those traces to improve learning”
  4. 4. Clow, LAK12, 2012
  5. 5. School dashboards (Google Images) • Maybe chop the first slide about this. 5
  6. 6. ELLI Teacher view 6 Buckingham Shum and Deakin Crick, 2012 (LAK12)
  7. 7. What data do we have about learners? • Demographics • Previous educational experience • Grades, scores, achievements, struggles • Attendance, location, gaze • Software logs • Online tracking • Other online activity (tracking) • … more every week. 7 Photo (CC)-BY-SA AJ Cann
  8. 8. What can we do with that data? • Identify learners who need help – Simple or predictive • Trigger interventions – Via teacher, or direct • Learn which interventions work • Build a complete cognitive learning system • Suggest resources or source of help – Learners like you found this helpful – This person might be able to help you 8 Photo (CC)-BY-NC Pulpolux
  9. 9. what should we do?
  10. 10. 10 control Photo (CC)-BY Andy Roberts surveillance
  11. 11. 11 support Photo (CC)-BY-NC-SA Drew Bennett guidance
  12. 12. Glasswinged butterfly, ? Greta oro Photo (CC)-BY-NC-ND by Greg Foster on Flickr Principles • Privacy • Data protection • Ethics • Transparency
  13. 13. Dutch perspective 13
  14. 14. Example 1: Math Garden (NL) 14
  15. 15. A garden that needs maintainens • From primary school – • Encourages weekly practice – otherwise the garden withers • Rewards: blooming plants and virtual coins (which can be exchanged) • Automatically adjusts the difficulty of math exercises to the ability level of the user 15
  16. 16. (SWE) • Videos (also with sub-titles in many languages), texts and quizzes • Feedback to both the student and the teacher 16
  17. 17. Interactive Flipped Classroom (SWE) • Based on the flipped classroom method • Makes the students more active in the classroom • But they are still quite passive when looking at videos • Technical platform which helps the teacher to cut the videos in smaller chunks and add quizzes • Also possible for students to stop the video and write questions to the teacher 17
  18. 18. Feedback to the teacher • How many have looked at the video (and when)? • How did the group score on the quizzez? • Which parts did they found (particularly) difficult? • Where they confused by the examples? • Specific questions feeded back to the teacher before the lesson 18
  19. 19. 19 Why do you subtract 4/9? The students can stop the video and write questions if needed
  20. 20. Regional initiative on drop-outs • Western parts of Sweden • When do student drop-out from upper secondary school? • Can we discover a pattern? • Using the data – analysing 20
  21. 21. Learning Analytics Community Exchange • Coordination and Support • Evidence Hub • Events • Publications, briefings, webinars
  22. 22. Activities and Achievements • Capturing the Latest Thinking – 18 blog posts on LACE website – Infographic 22
  23. 23. Evidence Hub 23 • Manage content and metadata of the web presence and knowledge base (the Evidence Hub) to create a valued and used resource • Identify, collect and synthesise claims and evidence for the benefits of learning analytics
  24. 24. Evidence Hub – Rationale • Add value on top of bibliographies (Mendeley) & machine analysis of LAK Dataset • Support for practitioners, researchers, managers, policymakers • Evidence for or against a proposition / hypothesis • Propositions developed among partners & at SoLAR Flare UK 24 Photo (CC)-BY Inessa Achmedova
  25. 25. Evidence Hub – Propositions A. Learning analytics improve learning outcomes B. Learning analytics improve learning support and teaching, including retention, completion and progression C. Learning analytics are taken up and used widely, including deployment at scale. D. Learning analytics are used in an ethical way 25
  26. 26. Evidence Hub – Evidence Flow map 26
  27. 27. Evidence Hub – Map of Evidence 27
  28. 28. Evidence Hub – Outlook • Also capture material with a lower threshold – Projects – Activity – Opinion and policy • Easier to submit & quality control • Broader view of field • Not already available elsewhere • Broader interest – Helps sustainability beyond the project 28
  29. 29. State of the art of LA in school sector • little systematic use of data at school level • more at national and municipal level • lack of knowledge in the school community • some interest • increasing vendor interest – new products • privacy big issue • little research found 29
  30. 30. Thanks! • Slides made by Doug Clow, Jan Hylén and myself 30
  31. 31. 31 CC-BY – You are free to: copy, share, adapt, or re-mix; photograph, film, or broadcast; blog, live-blog, or post video of this presentation provided that: You attribute the work to its authors and respect the rights and licences associated with its components.