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Supporting higher education in
integrating learning analytics
Dragan Gašević
@dgasevic
July 5, 2017
LASI Spain
Madrid, Spa...
Current state
Understanding & supporting learning
Moving away from deficit models
Learning analytics is about learning
Gašević, D., Dawson, S., Siemens, G. (2015). Let’s not forget: Learning analytics are...
Field of research and practice
Gašević, D., Kovanović, V., & Joksimović, S. (2017). Piecing the Learning Analytics Puzzle:...
Our institution is in
early days of adoption
ADOPTION CHALLENGES
Current state – Oz and Europe
http://sheilaproject.eu/http://he-analytics.com
Adoption challenge
Leadership for strategic
implementation & monitoring
Tsai, Y. S., & Gasevic, D. (2017). Learning analyt...
Adoption challenge
Equal engagement with
different stakeholders
Tsai, Y. S., & Gasevic, D. (2017). Learning analytics in h...
Adoption challenge
Training to cultivate data literacy
among primary stakeholders
Tsai, Y. S., & Gasevic, D. (2017). Learn...
Adoption challenge
Policies for learning analytics practice
Tsai, Y. S., & Gasevic, D. (2017). Learning analytics in highe...
What’s necessary to move forward?
DIRECTIONS
Data – Model – Transformation
Gašević, D., Dawson, S., Pardo, A. (2016). How do we start? State and Directions of Learning...
Inclusive adoption process
Inclusive adoption process
http://sheilaproject.eu/
Inclusive adoption process
Macfadyen, L., Dawson, S., Pardo, A., Gašević, D., (2014). The learning analytics imperative an...
SHEILA policy making framework
Action – Challenges – Policy – Instruments
http://sheilaproject.eu/
SHEILA policy making framework
Action – Challenges – Policy – Instruments
http://sheilaproject.eu/
SHEILA policy making framework
Macfadyen, L., Dawson, S., Pardo, A., Gašević, D., (2014). The learning analytics imperativ...
Step 1 – Map political context
Internal and external drivers for
learning analytics adoption
Step 1 – Map political context
One size fits all does not work in
learning analytics
Step 1 – Map political context
Opportunities to build learning
analytics on existing projects/practice
SHEILA policy making framework
Macfadyen, L., Dawson, S., Pardo, A., Gašević, D., (2014). The learning analytics imperativ...
Step 2 – Identify key stakeholders
Primary users of learning analytics
Step 2 – Identify key stakeholders
The project sponsor on
the senior management team
Step 2 – Identify key stakeholders
Other critical stakeholders to consider
Internal – professional and academic teams
Exte...
Students’ perspective
Students expect the use of their data
provided ethics & privacy consideration
Students’ perspective
But, they are not sure if teaching staff
will know to use learning analytics
Teaching staff’s perspective
Concerned about their workload
Contradictory views
Students and teaching staff
don’t share the same perspectives
Experts’ perspective
Privacy and ethics are most important
but easy to implement
SHEILA policy making framework
Macfadyen, L., Dawson, S., Pardo, A., Gašević, D., (2014). The learning analytics imperativ...
Step 3 – Identify desired behavior changes
Identify areas where decisions will be
informed by learning analytics
Step 3 – Identify desired behavior changes
Define responsibilities and
implications for primary users
Step 3 – Identify desired behavior changes
Identification of possible
inadvertent consequences
SHEILA policy making framework
Macfadyen, L., Dawson, S., Pardo, A., Gašević, D., (2014). The learning analytics imperativ...
Step 4 – Develop engagement strategy
Alignment of learning analytics with
the wider institutional strategies
Step 4 – Develop engagement strategy
Secure funding, establish a working
group, and raise awareness
Step 4 – Develop engagement strategy
Select data that will be fed back to users
Step 4 – Develop engagement strategy
How interventions will be triggered
and who is responsible?
SHEILA policy making framework
Macfadyen, L., Dawson, S., Pardo, A., Gašević, D., (2014). The learning analytics imperativ...
Step 5 – Analyze internal capacity
Data storage, disposal, and
security evaluation
Step 5 – Analyze internal capacity
Human, financial, legal, and
infrastructural capacity
Step 5 – Analyze internal capacity
Evaluate institutional culture
Trust in data
Decision-making based on data
Openness to ...
SHEILA policy making framework
Macfadyen, L., Dawson, S., Pardo, A., Gašević, D., (2014). The learning analytics imperativ...
Step 6 – Establish monitoring & learning frameworks
Establish qualitative and quantitative
indicators of success
Stage the...
Step 6 – Establish monitoring & learning frameworks
Seek feedback from primary users
through various channels
Step 6 – Establish monitoring & learning frameworks
Recognizing and addressing
limitations observed
Systemic Adoption Model
Colvin, C., et al. (2015). Student retention and learning analytics: A snapshot of Australian prac...
Solution-focused Model
Colvin, C., et al. (2015). Student retention and learning analytics: A snapshot of Australian pract...
Process-focused Model
Colvin, C., et al. (2015). Student retention and learning analytics: A snapshot of Australian practi...
How is innovation
recognized, supported, and promoted?
FINAL REMARKS
Learning analytics principles
Data incompleteness, bias perpetuation,
avoidance of deficit models, facilitation of trainin...
Learning analytics purposes
Quality, equity, personalized feedback, coping with scale,
student experience, skills, and eff...
Critical role of leadership for adoption
of learning analytics
Skill development must not be
underestimated
Promoting and supporting innovation
Development of institutional policy as
a critical enabler
Supporting higher education in
integrating learning analytics
Dragan Gašević
@dgasevic
June 20, 2017
Sydney, NSW, Australia
VII Jornadas eMadrid "Education in exponential times". "Supporting higher education in integrating learning analytics". Dr...
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VII Jornadas eMadrid "Education in exponential times". "Supporting higher education in integrating learning analytics". Dragan Gasevic. U Edinburgh, UK. 05/07/2017.

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VII Jornadas eMadrid "Education in exponential times". "Supporting higher education in integrating learning analytics". Dragan Gasevic. U Edinburgh, UK. 05/07/2017.

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VII Jornadas eMadrid "Education in exponential times". "Supporting higher education in integrating learning analytics". Dragan Gasevic. U Edinburgh, UK. 05/07/2017.

  1. 1. Supporting higher education in integrating learning analytics Dragan Gašević @dgasevic July 5, 2017 LASI Spain Madrid, Spain 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. Data – Model – Transformation Gašević, D., Dawson, S., Pardo, A. (2016). How do we start? State and Directions of Learning Analytics Adoption. Oslo, Norway: International Council for Open and Distance Education. http://bit.ly/icde_la_16
  15. 15. Inclusive adoption process
  16. 16. Inclusive adoption process http://sheilaproject.eu/
  17. 17. 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.
  18. 18. SHEILA policy making framework Action – Challenges – Policy – Instruments http://sheilaproject.eu/
  19. 19. SHEILA policy making framework Action – Challenges – Policy – Instruments http://sheilaproject.eu/
  20. 20. SHEILA policy making framework 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.
  21. 21. Step 1 – Map political context Internal and external drivers for learning analytics adoption
  22. 22. Step 1 – Map political context One size fits all does not work in learning analytics
  23. 23. Step 1 – Map political context Opportunities to build learning analytics on existing projects/practice
  24. 24. SHEILA policy making framework 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.
  25. 25. Step 2 – Identify key stakeholders Primary users of learning analytics
  26. 26. Step 2 – Identify key stakeholders The project sponsor on the senior management team
  27. 27. Step 2 – Identify key stakeholders Other critical stakeholders to consider Internal – professional and academic teams External – service providers/vendors and collaborators Champions of learning analytics (bottom up)
  28. 28. Students’ perspective Students expect the use of their data provided ethics & privacy consideration
  29. 29. Students’ perspective But, they are not sure if teaching staff will know to use learning analytics
  30. 30. Teaching staff’s perspective Concerned about their workload
  31. 31. Contradictory views Students and teaching staff don’t share the same perspectives
  32. 32. Experts’ perspective Privacy and ethics are most important but easy to implement
  33. 33. SHEILA policy making framework 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.
  34. 34. Step 3 – Identify desired behavior changes Identify areas where decisions will be informed by learning analytics
  35. 35. Step 3 – Identify desired behavior changes Define responsibilities and implications for primary users
  36. 36. Step 3 – Identify desired behavior changes Identification of possible inadvertent consequences
  37. 37. SHEILA policy making framework 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.
  38. 38. Step 4 – Develop engagement strategy Alignment of learning analytics with the wider institutional strategies
  39. 39. Step 4 – Develop engagement strategy Secure funding, establish a working group, and raise awareness
  40. 40. Step 4 – Develop engagement strategy Select data that will be fed back to users
  41. 41. Step 4 – Develop engagement strategy How interventions will be triggered and who is responsible?
  42. 42. SHEILA policy making framework 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.
  43. 43. Step 5 – Analyze internal capacity Data storage, disposal, and security evaluation
  44. 44. Step 5 – Analyze internal capacity Human, financial, legal, and infrastructural capacity
  45. 45. Step 5 – Analyze internal capacity Evaluate institutional culture Trust in data Decision-making based on data Openness to changes and innovation
  46. 46. SHEILA policy making framework 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.
  47. 47. Step 6 – Establish monitoring & learning frameworks Establish qualitative and quantitative indicators of success Stage the process to recognize institutional development
  48. 48. Step 6 – Establish monitoring & learning frameworks Seek feedback from primary users through various channels
  49. 49. Step 6 – Establish monitoring & learning frameworks Recognizing and addressing limitations observed
  50. 50. Systemic Adoption Model Colvin, C., et al. (2015). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Sydney: Australian Office for Learning and Teaching.
  51. 51. Solution-focused Model Colvin, C., et al. (2015). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Sydney: Australian Office for Learning and Teaching.
  52. 52. Process-focused Model Colvin, C., et al. (2015). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Sydney: Australian Office for Learning and Teaching.
  53. 53. How is innovation recognized, supported, and promoted?
  54. 54. FINAL REMARKS
  55. 55. Learning analytics principles Data incompleteness, bias perpetuation, avoidance of deficit models, facilitation of training, not used for performance assessment The University of Edinburgh (2017). Learning Analytics Policy, http://www.ed.ac.uk/academic-services/projects/learning-analytics-policy
  56. 56. Learning analytics purposes Quality, equity, personalized feedback, coping with scale, student experience, skills, and efficiency The University of Edinburgh (2017). Learning Analytics Policy, http://www.ed.ac.uk/academic-services/projects/learning-analytics-policy
  57. 57. Critical role of leadership for adoption of learning analytics
  58. 58. Skill development must not be underestimated
  59. 59. Promoting and supporting innovation
  60. 60. Development of institutional policy as a critical enabler
  61. 61. Supporting higher education in integrating learning analytics Dragan Gašević @dgasevic June 20, 2017 Sydney, NSW, Australia

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