five steps for achieving learning analytics success

Five Steps for Achieving

(Learning) Analytics Success

Ellen D. Wagner Ph.D. Chief Research and Strategy Officer

PAR Framework

@edwsonoma edwsonoma@gmail.com

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.    

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  =  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  

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,  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.  

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  

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  

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  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  

Analysis/Modeling Process

•  Analysis  and  model  building  is  an  itera7ve  process  

•  Around  70-­‐80%  efforts  are  spent  on  data  explora*on  and  understanding.  

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.com/public/ins*tu*ons/par    

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    

Faculty  

Student  Success  

IT  

Academic  Affairs  

Enrollment  Management  

Financial  Aid  

Ins*tu*onal  Research  

PAR  is  redefining    ins*tu*onal  conversa*ons  

Students  

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://parframework.org    Ellen  Wagner:  Twi>er  h>p://twi>er.com/edwsonoma  Google+  edwsonoma  On  email  edwsonoma@gmail.com