Five Steps for Achieving
(Learning) Analytics Success
Ellen D. Wagner Ph.D.
Chief Research and Strategy Officer
PAR Framew...
Common Definitions for Today
Data	
  is	
  informa*on,	
  everywhere.	
  It	
  comes	
  in	
  all	
  kinds	
  and	
  shape...
DATA ARE CHANGING EVERYTHING
Google Trends: Analytics
Google Trends: Big Data
h>p://bit.ly/1goTBmP	
  
1	
  Gigabyte	
  	
  =	
  1,024	
  Megabytes	
  	
  
1	
  Terabyte	
  =	
  1,024	
  Gigabytes	
  	
  
1	
  Petabyte	
  =	
...
h>p://bit.ly/1goTBmP	
  
h>p://bit.ly/1goTBmP	
  
h>p://bit.ly/1goTBmP	
  
Making	
  Sense	
  of	
  All	
  The	
  Data	
  
Data Readiness in Higher Ed
Analy*cs	
  have	
  ramped	
  up	
  everyone’s	
  expecta*ons	
  of	
  
personaliza*on,	
  acc...
While Big Data raise expectations,
student data drive big decisions in .edu
Costs and Completion Rates
Source:	
  	
  New	
  York	
  Times;	
  NCES	
  
0	
  
10	
  
20	
  
30	
  
40	
  
50	
  
60	
 ...
Performance Based Funding
h>p://www.ncsl.org/issues-­‐research/educ/performance-­‐funding.aspx	
  
Institutional Accountability
h>p://www.whitehouse.gov/issues/educa*on/higher-­‐educa*on/college-­‐score-­‐card	
  
Google Trends: Learning Analytics
Google Trends: Predictive Analytics
What do we want?
The RIGHT Answers!!
When	
  do	
  we	
  want	
  them?	
  NOW!!	
  
The Predictive Analytics Reporting
(PAR) Framework
•  PAR	
  is	
  a	
  na*onal,	
  non-­‐profit	
  mul*-­‐ins*tu*onal	
  
...
Analysis/Modeling Process
•  Analysis	
  and	
  model	
  building	
  is	
  an	
  
itera7ve	
  process	
  
•  Around	
  70-...
Structured, Readily Available Data
•  Common	
  data	
  
defini*ons	
  =	
  reusable	
  
predic*ve	
  models	
  and	
  
mea...
PAR Outputs
Descrip7ve	
  	
  
Benchmarks	
  	
  
Show	
  how	
  ins*tu*ons	
  
compare	
  to	
  their	
  peers	
  in	
  
...
Faculty	
  
Student	
  
Success	
  
IT	
  
Academic	
  
Affairs	
  
Enrollment	
  
Management	
  
Financial	
  Aid	
  
Ins*...
5	
  Steps	
  For	
  Achieving	
  	
  
(Learning)	
  Analy*cs	
  Success	
  
START WITH AN EYE ON
YOUR OUTCOMES.
BE CLEAR ABOUT WHAT YOU
MEAN BY SUCCESS.
COMMON DEFINITIONS ENABLE
SHARED UNDERSTANDING.
FOCUS ON INSIGHTS,
NOT JUST ON DATA.
SHARE YOUR WORK
With	
  thanks	
  to	
  
Jane	
  Bozarth,	
  2014	
  
THANK YOU FOR YOUR
INTEREST
For	
  more	
  informa*on	
  about	
  PAR	
  please	
  visit	
  our	
  website:	
  
h>p://parf...
Five Steps for Achieving Learning Analytics Success
Five Steps for Achieving Learning Analytics Success
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Five Steps for Achieving Learning Analytics Success

  1. 1. Five Steps for Achieving (Learning) Analytics Success Ellen D. Wagner Ph.D. Chief Research and Strategy Officer PAR Framework @edwsonoma edwsonoma@gmail.com
  2. 2. Common Definitions for Today Data  is  informa*on,  everywhere.  It  comes  in  all  kinds  and  shapes   and  sizes.    It’s  not  all  digital,  but  most  of  it  is.   Analy(cs  are  methods  and  tools  to  parse  streams  of  digital  bits   and  bytes  into  meaningful  pa>erns  that  can  be  explored  to  help   stakeholders  make  more  effec*ve  decisions.   Learning  analy(cs  are  methods  and  tools  needed  to  parse  the   stream  of  digital  bits  into  meaningful  pa>erns  that  explore   dimensions  of  cogni*on,  instruc*on  and  academic  experience,   including  student  success.     Data-­‐readiness  ranges  from  essen*al  individual  knowledge  and   skills  to  ins*tu*onal  capacity  for  crea*ng  a  culture  that  values   evidence-­‐based  decision-­‐making.    
  3. 3. DATA ARE CHANGING EVERYTHING
  4. 4. Google Trends: Analytics
  5. 5. Google Trends: Big Data
  6. 6. h>p://bit.ly/1goTBmP  
  7. 7. 1  Gigabyte    =  1,024  Megabytes     1  Terabyte  =  1,024  Gigabytes     1  Petabyte  =  1,024  Terabytes   1  Exabyte  =  1,024  Petabytes     1  Ze>abyte  =  1,024  Exabytes     1  Yo>abyte  =  1,024  Ze>abytes   1  ZB  –  1,000,000,000,000,000,000,000  bytes  
  8. 8. h>p://bit.ly/1goTBmP  
  9. 9. h>p://bit.ly/1goTBmP  
  10. 10. h>p://bit.ly/1goTBmP  
  11. 11. Making  Sense  of  All  The  Data  
  12. 12. Data Readiness in Higher Ed Analy*cs  have  ramped  up  everyone’s  expecta*ons  of   personaliza*on,  accountability  and  transparency.   Academic  enterprises  simply  cannot  live  outside  the   ins*tu*onal  focus  on  tangible,  measurable  results   driving  IT,  finance,  recruitment  and  other  mission   cri*cal  concerns.  
  13. 13. While Big Data raise expectations, student data drive big decisions in .edu
  14. 14. Costs and Completion Rates Source:    New  York  Times;  NCES   0   10   20   30   40   50   60   70   1996   1997   1998   1999   2000   2001   2002   2003   2004   2005   2-­‐yr  colleges   4-­‐yr  colleges   Gradua7on  rates  at  150%  of  7me   Cohort  year  
  15. 15. Performance Based Funding h>p://www.ncsl.org/issues-­‐research/educ/performance-­‐funding.aspx  
  16. 16. Institutional Accountability h>p://www.whitehouse.gov/issues/educa*on/higher-­‐educa*on/college-­‐score-­‐card  
  17. 17. Google Trends: Learning Analytics
  18. 18. Google Trends: Predictive Analytics
  19. 19. What do we want? The RIGHT Answers!! When  do  we  want  them?  NOW!!  
  20. 20. The Predictive Analytics Reporting (PAR) Framework •  PAR  is  a  na*onal,  non-­‐profit  mul*-­‐ins*tu*onal   collabora*ve  focused  on  ins*tu*onal  effec*veness  and   student  success.   •  PAR  is  a  “big  data”  analysis  effort  using  predic7ve   analy7cs  to  iden*fy  drivers  related  to  loss  and  momentum   and  to  inform  student  loss  preven7on     •  PAR  member  ins*tu*ons  voluntarily  contribute  de-­‐ iden7fied  student  records  to  create  a  single  federated   database.   •  Descrip*ve,  inferen*al  and  predic*ve  analyses  have  been   used  to  create  benchmarks,  ins*tu*onal  predic7ve  models   and  to  map  student  success  interven7ons  to  predictor   behaviors  
  21. 21. Analysis/Modeling Process •  Analysis  and  model  building  is  an   itera7ve  process   •  Around  70-­‐80%  efforts  are  spent   on  data  explora*on  and   understanding.    
  22. 22. Structured, Readily Available Data •  Common  data   defini*ons  =  reusable   predic*ve  models  and   meaningful   comparisons.     •  Openly  published  via  a   cc  license  @   h>ps:// public.datacookbook.co m/public/ins*tu*ons/ par    
  23. 23. PAR Outputs Descrip7ve     Benchmarks     Show  how  ins*tu*ons   compare  to  their  peers  in   student  outcomes,  by   scaling  a  mul7-­‐ ins7tu7onal  database   for  benchmarking  and   research  purposes.     Predic7ve     Models     Iden*fy  which  students   need  assistance,  by  using   in-­‐depth,  ins7tu7onal   specific  predic7ve  models.     Models  are  unique  to  the   needs  and  priori*es  of  our   member  ins*tu*ons  based   on  their  specific  data.       Ins*tu*ons    address   areas  of  weakness   iden*fied  in    benchmarks   and  models  by  scaling   and  leveraging  a   member,  data  and   literature  validated   framework  for   examining  interven*ons   within  and  across   ins*tu*ons    (SSMx)     Interven7on     Matrix    
  24. 24. Faculty   Student   Success   IT   Academic   Affairs   Enrollment   Management   Financial  Aid   Ins*tu*onal   Research   PAR  is  redefining     ins*tu*onal  conversa*ons   Students  
  25. 25. 5  Steps  For  Achieving     (Learning)  Analy*cs  Success  
  26. 26. START WITH AN EYE ON YOUR OUTCOMES.
  27. 27. BE CLEAR ABOUT WHAT YOU MEAN BY SUCCESS.
  28. 28. COMMON DEFINITIONS ENABLE SHARED UNDERSTANDING.
  29. 29. FOCUS ON INSIGHTS, NOT JUST ON DATA.
  30. 30. SHARE YOUR WORK With  thanks  to   Jane  Bozarth,  2014  
  31. 31. THANK YOU FOR YOUR INTEREST For  more  informa*on  about  PAR  please  visit  our  website:   h>p://parframework.org     Ellen  Wagner:   Twi>er  h>p://twi>er.com/edwsonoma   Google+  edwsonoma   On  email  edwsonoma@gmail.com      

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