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Complexification of Higher Education

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Presented at XLi Conference, Billings, Montana, March 22, 2013.

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Complexification of Higher Education

  1. 1. The Complexification of Higher Education George Siemens, PhD March 22, 2013 Xli 2013 Billings, Montana
  2. 2. Higher education is diversifying:Student profilesLearning needsAcademic programsAssessmentThe narrative of higher education is no longersingular, reflective of complexification of thesector.
  3. 3. Openness
  4. 4. Allen & Seaman, 2012
  5. 5. Annual volumes of articles in full immediate open access journals Laakso and Björk BMC Medicine 2012 10:124
  6. 6. Nature, 2013
  7. 7. Technology
  8. 8. http://www.scribd.com/doc/48586092/KPCB-Top-10-Mobile-Trends
  9. 9. Education Sector Factbook, 2012
  10. 10. Allen & Seaman 2011
  11. 11. SaaS PaaS IaaSCloud Computing
  12. 12. Entrepreneurship & Startups
  13. 13. On the Last Digital Frontier Investors give education technology firms the nod Joseph Wilson, Special to Financial Post | Sep 10, 2012
  14. 14. Ed-tech startupsWith transformations already underway in news,music, videos/movies, startup gold rush nowturning focus to education
  15. 15. IBIS Capital: Global e-Learning Investment Review, 2013
  16. 16. Economic
  17. 17. Center on Budget andPolicy Priorities, 2013
  18. 18. An Agenda for Australian Higher Education, 2013-2016
  19. 19. The Conference Board
  20. 20. McKinsey Quarterly, 2012
  21. 21. Moody’s Investor Services, 2013
  22. 22. NYTimes, UNESCO Data
  23. 23. Student Profiles
  24. 24. Increasing diversity of student profilesThe U.S. is now in a position when less thanhalf of students could be considered fulltimestudents. In other words, students who canattend campus five days a week nine-to-five,are now a minority. (Bates, 2013)
  25. 25. Certificates- Fastest growing form of credentialing(800% increase in 30 years)- Industry-facing Carnevale, Rose, Hanson 2012
  26. 26. Learning Analytics
  27. 27. “The slightest move in the virtual landscapehas to be paid for in lines of code” Bruno Latour
  28. 28. Data trails revealsour sentiments,our attitudes,our social connections,our intentions,what we know,how we learn,and what we might do next.
  29. 29. Focus of analytics Who Benefits?Course-level: social networks, Learners, facultyconceptual development, languageanalysisAggregate (big data) predictive Learners, facultymodeling, patterns of success/failureInstitutional: learner profiles, Administrators, IR, funders,performance of academics, resource marketingallocationRegional & National (state/provincial): Governments, administratorscomparisons between systemsInternational: ‘world class universities’ National governments (OECD),
  30. 30. SNAPP
  31. 31. Denley, 2012
  32. 32. Rio Salado CollegeStudent Support Model Image source: EDUCAUSE
  33. 33. Persistence Plus Image source: EDUCAUSE
  34. 34. Granularization:Courses to Competencies
  35. 35. The American Council on Education “says itwants more students to earn college creditfor learning that occurs outside thecollege classroom. Some of these creditpathways are trendy and new; others havebeen around for decades.” Inside Higher Ed, 2013
  36. 36. State of Wisconsin, 2012
  37. 37. State of Wisconsin, 2012
  38. 38. State of Wisconsin, 2012
  39. 39. “Pay for performance” education?Shifting (sharing) responsibility for studentsuccess with content providers?
  40. 40. Knowledge in pieces diSessa, 1993
  41. 41. Future(s) of higher education
  42. 42. “The future looks like this: Access to college-level education will befree for everyone; the residential college campus will becomelargely obsolete; tens of thousands of professors will lose theirjobs; the bachelor’s degree will become increasingly irrelevant; andten years from now Harvard will enroll ten million students.”
  43. 43. "We have 10,000 colleges in this country, so when you get down to thevery bottom, [a qualification] is worth nothing…a fair fraction of thevery bad universities in the US will disappear. It may take 10 years, itmay take 20 years, but that is going to happen."
  44. 44. Status QuoRisk: HighIntelligence level of this strategy: StupidImpact: Loss of relevance
  45. 45. Accreditors (teach globally, accredit locally)Risk: mediumIntelligence level of this strategy: okImpact: Loss of control of teaching,competition with other accreditors (incl.for-profit). Fragmentation of learnerexperience. Loss of brand.
  46. 46. Localized/specializedRisk: lowIntelligence level of this strategy: kinda smartImpact: play to strengths of local system.Community. Retain local presence. Brandloyalty
  47. 47. Partnership modelRisk: mediumIntelligence level of this strategy: kinda smartImpact: Employment-focused. Politicallyrelevant. Learner interest is high.
  48. 48. Net ModelRisk: High (short term). Low (long term)Intelligence level of this strategy: smartImpact: Expensive. Analytics-driven. Culturalshift needed. Risk of moving too early.University as new integrator. Branddevelopment
  49. 49. EdTech Innovation Conference Calgary, May 1-3, 2013 www.edinnovation.ca
  50. 50. Twitter/Gmail: gsiemens

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