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MedU Plenary Session on Analytics

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An interactive plenary session on learning analytics for the MedU Consortium.

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MedU Plenary Session on Analytics

  1. 1. Analytics in Decision-Making: Learners, Learning, Life & Analytics What Matters for Learning Analytics? Janet Corral, PhD University of Colorado School of Medicine
  2. 2. Analytics is a Hot Topic 2011 Journal of Learning Analytics 2012 Georgia State Univ launches student data system 2013 1st international learning analytics conference 2014 Two analytics presentations at AAMC LSL 2015 1st AAMC GIR DATA Symposium 2016 EDUCAUSE Horizon report: Learning analytics 1 yr or less to adoption 2017 1st Ed Tech Working Group presentation on data standards 2010 Purdue University Signals Higher Education Med Schools
  3. 3. Defining analytics Learning Analytics Education Data Mining Academic Analytics Corral, J. (2017) based on Siemens & Baker (2012)
  4. 4. Defining analytics Learning Analytics Education Data Mining Academic Analytics Corral, J. (2017) based on Siemens & Baker (2012) Reasoning forward Investigating from the data Business intelligence
  5. 5. Need multiple data sources To support quality learning outcomes, educational analytics…
  6. 6. Need multiple data sources Will impact the whole system of stakeholders To support quality learning outcomes, educational analytics…
  7. 7. Analytics in Decision-Making: Learners, Learning, Life & Analytics What does this mean for Aquifer?
  8. 8. Continuum of Options Feedback Prediction Coaching
  9. 9. FEEDBACK
  10. 10. With gratitude to NYU Educational Data Warehouse: alex.support@med.nyu.edu
  11. 11. Vanderbilt VSTAR https://vstar.mc.vanderbilt.edu/
  12. 12. Vanderbilt VSTAR https://vstar.mc.vanderbilt.edu/
  13. 13. Feedback
  14. 14. Feedback
  15. 15. Action is outside the digital system
  16. 16. Analytics is more than building dashboards
  17. 17. Learning analytics impact our systems of Education
  18. 18. Analytics in Decision-Making: Learners, Learning, Life & Analytics How would you enhance the feedback on one Aquifer VP? What data metrics are important?
  19. 19. Prediction
  20. 20. Who will complete?
  21. 21. Prediction of learning requires work Pusic, V. et al (2015). Learning curves in health professions education.
  22. 22. Does feedback work?
  23. 23. Student Learning Pathways Corral, J. (2012). Learning with Virtual Patients. ! !
  24. 24. Nudging & Coaching
  25. 25. Nudging
  26. 26. Nudging It’s been 7 days since you logged in. Students who complete VPs 3x/week are 17% more successful in the clerkship.
  27. 27. Coaching Credit: University of Michigan
  28. 28. Student Learning Pathways Corral, J. (2012). Learning with Virtual Patients. Students who had difficulty here, found it helpful to: 1. Read _____ 2. Do cases 1 & 5
  29. 29. Analytics in Decision-Making: Learners, Learning, Life & Analytics What analytics would you add to a series of VPs? What does that type of analytics look like across the VPs? What data metrics are important? Feedback Prediction Coaching
  30. 30. Other Considerations
  31. 31. UC Denver 81 dentistry students 184 medical students 180 pharmacy students Denver Public Schools 81,438 students Facebook 1.71 billion monthly active users University of Phoenix 400,000 students per year Twitter 14 Pedabytes of Data Netflix 40 Pedabytes of Data EDU Data ≠ not BIG Data
  32. 32. Impact is on the System 10.37% 100 Student Advisors or Coaches increase in B or A awarded “The students who get a red light almost all contact me immediately to ask how to raise their grades.” -Tim Delworth, Mathematics
  33. 33. Impact is on the System NEW faculty role ‘coach’ NEWcurriculum
  34. 34. Learning is social Network Awareness Tool of Social Learn Network (Teplov, 2012)
  35. 35. Learning analytics can detect when emotions impact achievement Delight, Surprise (Rare) Frustration (less associated with poorer learning) Confusion, Engaged Concentration (Watch out for boredom!) Baker, D’Mello, Rodrigo, & Graesser (2004, 2010)
  36. 36. Learning analytics can detect when emotions impact achievement Delight, Surprise (Rare) Frustration (less associated with poorer learning) Confusion, Engaged Concentration (Watch out for boredom!) Baker, D’Mello, Rodrigo, & Graesser (2004, 2010)
  37. 37. Need multiple data sources To support quality learning outcomes, educational analytics…
  38. 38. Need multiple data sources Will impact the whole system of stakeholders To support quality learning outcomes, educational analytics…
  39. 39. Aquifer is positioned for success
  40. 40. Discussion for the Aquifer Community What type(s) of analytics are most effective for Aquifer to engage?
  41. 41. Janet Corral Janet.Corral@ucdenver.edu Thank you

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