Overview of analytics for the Gateways to Completion Community of Practice meeting, March 22, 2014

1. PREDICTIVE ANALYTICS
OVERVIEW / PREVIEW
Matthew D. Pistilli, Ph.D.
Research Scientist
Office of Institutional Research, Assessment & Evaluation
Purdue University
mdpistilli@purdue.edu | @mdpistilli
March 22, 2014

5. Challenge: How do you find the student at risk?
http://www.youthareawesome.com/wp-content/uploads/2010/10/wheres-waldo1.jpg

6. http://www.youthareawesome.com/wp-content/uploads/2010/10/wheres-waldo1.jpg
Challenge: How do you find the student at risk?

7. http://classhack.com/post/76426005382/waldo

8. • Actionable intelligence
• Moving research to practice
• Basis for design, pedagogy,
self-awareness
• Changing institutional culture
• Understanding the limitations
and risks
Analytics is
about...

9. DEFINITIONS
 Using analytic techniques to help target
instructional, curricular, and support resources to
support the achievement of specific learning goals
(van Bareneveld, Arnold, & Campbell, 2012)
 the process of developing actionable insights
through problem definition and the application of
statistical models and analysis against existing
and/or simulated future data (Cooper, 2012)

10. http://www.gravitatedesign.com/wp/wp-content/uploads/SEO-data.jpg

11. THE BIG QUESTIONS
 What can institutions do to improve student
success?
 How can institutions help students take
advantage of existing campus resources?
 What existing information on campus can be
utilized to better identify students at risk?
 How can students become self-aware of what
effort is necessary to be successful in college?
 How can analytics make a strategic impact at
scale?

12. ANALYTICS IN G2C

13. OUR PREMISE
 Ambient data
 Parsimony
 Focused on students

14. THE DEVELOPMENT PROCESS
 Basic model constructed
 Four institutions to provide data for model building
and testing
 Model to be tested, revised, retested, revised, etc.
 Anticipated roll out early summer
 Anticipated use by institutions this fall

15. MODEL BASICS
 5 “buckets” of data
 Each bucket weighted
 Largest weight placed on current academic performance
and interaction with the course
 The buckets:
 Student academic effort
 Current student performance
 Historical student performance
 Student demographics
 Student behavior out of class
 Specific data to be used TBD based on model
testing

16. WORTH
NOTING…

17. http://i.imgur.com/nZArTnc.jpg

18. EXPECTATIONS REALITY
 Plug and Play
 Immediate results
 Solve every problem –
ever!
 Universal adoption
 Everyone would love it!
 Fits, starts, reboots
 Mostly long term outcomes
 Solve some problems,
create some new problems
 Lackluster use
 Not everyone loved it

19. RESULTS… A LONG TIME COMING
 Immediate
 Few
 Maybe noticed by instructors
 Possibly noticed by help centers
 Short term (1 term out)
 Some
 Based in final grades earned compared to previous terms
 Medium term (2 terms out)
 A few more
 Success of students in sequential courses
 One-year retention now available
 Long term (3-4 years out)
 Retention over time knowable
 Graduation rates now available

20. INSTITUTIONAL CHALLENGES
 Data in many places, “owned” by many
people/organizations
 Different processes, procedures, and regulations
depending on data owner
 Everyone can see potential, but all want something
slightly different
 Sustainability – “can’t you just…”
 Faculty participation is essential
 Staffing is a challenge

21. NEW POSSIBILITIES
 Using data that exists on campus
 Taking advantages of existing programs
 Bringing a “complete picture” beyond academics
 Focusing on the “Action” in “Actionable Intelligence”

22. PREDICTIVE ANALYTICS
OVERVIEW / PREVIEW
Matthew D. Pistilli, Ph.D.
Research Scientist
Office of Institutional Research, Assessment & Evaluation
Purdue University
mdpistilli@purdue.edu | @mdpistilli
March 22, 2014