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Let’s get there! Towards policy for adoption of learning analytics

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The field learning analytics is established with the promise for the education sector to embrace the use of data for decision making. There are many examples of successful use of learning analytics to enhance student experience, increase learning outcomes, and optimize learning environments. Despite much interest in learning analytics, many higher education institutions are still looking for effective ways that can enable systemic uptake. The talk will first describe some selected examples of the successful use of learning analytics in higher education. Key challenges identified to affect implementation of learning analytics will then be discussed. This will be followed with an overview of an approach to the development of institutional policy and strategy for the learning analytics implementation in higher education. The talk will be based on the findings of several international studies and will critically interrogate the role of institutional and cultural differences.

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Let’s get there! Towards policy for adoption of learning analytics

  1. 1. Let’s get there! Towards policy for adoption of learning analytics Dragan Gašević @dgasevic Oct 26, 2017 LSAC 2017 Amsterdam, The Netherlands http://sheilaproject.eu/
  2. 2. Current state Understanding & supporting learning Moving away from deficit models
  3. 3. Learning analytics is about learning Gašević, D., Dawson, S., Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.
  4. 4. Field of research and practice Gašević, D., Kovanović, V., & Joksimović, S. (2017). Piecing the Learning Analytics Puzzle: A Consolidated Model of a Field of Research and Practice. Learning: Research and Practice, 3(2), 63-78. doi:10.1080/23735082.2017.1286142
  5. 5. Our institution is in early days of adoption
  6. 6. ADOPTION CHALLENGES
  7. 7. Current state – Oz and Europe http://sheilaproject.eu/http://he-analytics.com
  8. 8. Adoption challenge Leadership for strategic implementation & monitoring Tsai, Y. S., & Gasevic, D. (2017). Learning analytics in higher education – challenges and policies: a review of eight learning analytics policies. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 233-242).
  9. 9. Adoption challenge Equal engagement with different stakeholders Tsai, Y. S., & Gasevic, D. (2017). Learning analytics in higher education – challenges and policies: a review of eight learning analytics policies. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 233-242).
  10. 10. Adoption challenge Training to cultivate data literacy among primary stakeholders Tsai, Y. S., & Gasevic, D. (2017). Learning analytics in higher education – challenges and policies: a review of eight learning analytics policies. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 233-242).
  11. 11. Adoption challenge Policies for learning analytics practice Tsai, Y. S., & Gasevic, D. (2017). Learning analytics in higher education – challenges and policies: a review of eight learning analytics policies. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 233-242).
  12. 12. What’s necessary to move forward?
  13. 13. DIRECTIONS
  14. 14. Inclusive adoption process
  15. 15. Inclusive adoption process http://sheilaproject.eu/
  16. 16. Inclusive adoption process Macfadyen, L., Dawson, S., Pardo, A., Gašević, D., (2014). The learning analytics imperative and the sociotechnical challenge: Policy for complex systems. Research & Practice in Assessment, 9(Winter 2014), 17-28.
  17. 17. Methodology Literature - Policy - Adoption Academic staff - Survey - Focus groups Students - Survey - Focus groups Senior managers - Survey - Interviews Experts - Group concept mapping Policy framework Institutional policy/strategy Other stakeh. - Workshops - Committees
  18. 18. SHEILA policy framework http://sheilaproject.eu/
  19. 19. SHEILA policy framework http://sheilaproject.eu/
  20. 20. SHEILA policy framework http://sheilaproject.eu/
  21. 21. Map political context Internal and external drivers for learning analytics adoption
  22. 22. Map political context One size fits all does not work in learning analytics
  23. 23. Map political context Opportunities to build learning analytics on existing projects/practice
  24. 24. SHEILA policy framework http://sheilaproject.eu/
  25. 25. Identify key stakeholders The project sponsor on the senior management team
  26. 26. Students’ perspective Students expect the use of their data provided ethics & privacy is assured
  27. 27. Students’ perspective But, they are not sure if teaching staff will know to use learning analytics
  28. 28. Teaching staff’s perspective Concerned about their workload
  29. 29. Contradictory views Students and teaching staff don’t share the same perspectives
  30. 30. Experts’ perspective Privacy and ethics are most important but easy to implement
  31. 31. Experts’ perspective Purpose, ethics, and privacy need to be addressed first
  32. 32. SHEILA policy framework http://sheilaproject.eu/
  33. 33. Identify desired behavior changes Identify areas where decisions will be informed by learning analytics
  34. 34. Identify desired behavior changes Define implications for primary users
  35. 35. Identify desired behavior changes Identification of possible inadvertent consequences
  36. 36. SHEILA policy framework http://sheilaproject.eu/
  37. 37. Develop engagement strategy Alignment of learning analytics with the wider institutional strategies
  38. 38. Develop engagement strategy Secure funding, establish a working group, and raise awareness
  39. 39. Develop engagement strategy Select data that will be fed back to users Avoid impression of surveillance Prevent data overload
  40. 40. Develop engagement strategy How interventions will be triggered and who is responsible?
  41. 41. SHEILA policy framework http://sheilaproject.eu/
  42. 42. Analyze internal capacity Data storage, disposal, and security evaluation
  43. 43. Analyze internal capacity Human, financial, and infrastructural capacity
  44. 44. Analyze internal capacity What about empirical research about data privacy? Privacy paradox Barth, S., & de Jong, M. D. T. (2017). The privacy paradox – Investigating discrepancies between expressed privacy concerns and actual online behavior – A systematic literature review. Telematics and Informatics, 34(7), 1038-1058. https://doi.org/10.1016/j.tele.2017.04.013
  45. 45. Analyze internal capacity What do we do with GDPR 2016/679?
  46. 46. Analyze internal capacity Evaluate institutional culture Trust in data Decision-making based on data Openness to changes and innovation
  47. 47. SHEILA policy framework http://sheilaproject.eu/
  48. 48. Establish monitoring & learning frameworks Highly immature and few actual examples
  49. 49. Establish monitoring & learning frameworks Establish qualitative and quantitative indicators of success Stage the process to recognize institutional development
  50. 50. Establish monitoring & learning frameworks Seek feedback from primary users through various channels
  51. 51. Establish monitoring & learning frameworks Isolating the effect of learning analytics against other initiatives
  52. 52. FINAL REMARKS
  53. 53. Embracing complexity of educational systems
  54. 54. One size fits all does not work in learning analytics
  55. 55. Critical role of leadership for adoption of learning analytics
  56. 56. Promoting and supporting innovation
  57. 57. Let’s get there! Towards policy for adoption of learning analytics Dragan Gašević @dgasevic Oct 26, 2017 LSAC 2017 Amsterdam, The Netherlands http://sheilaproject.eu/

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