PREDICTIVE ANALYTICS OVERVIEW

/ PREVIEW

Matthew D. Pistilli, Ph.D.Research ScientistOffice of Institutional Research, Assessment & Evaluation Purdue University

mdpistilli@purdue.edu | @mdpistilli

March 22, 2014

Challenge: How do you find the student at risk?

http://www.youthareawesome.com/wp-content/uploads/2010/10/wheres-waldo1.jpg

http://www.youthareawesome.com/wp-content/uploads/2010/10/wheres-waldo1.jpg

Challenge: How do you find the student at risk?

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

• Actionable intelligence• Moving research to practice• Basis for design, pedagogy,

self-awareness• Changing institutional

culture• Understanding the

limitations and risks

Analytics is about...

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)

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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?

ANALYTICS IN G2C

OUR PREMISE

Ambient data Parsimony Focused on students

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

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

WORTH NOTING…

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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

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

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

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”

PREDICTIVE ANALYTICS OVERVIEW

/ PREVIEW

Matthew D. Pistilli, Ph.D.Research ScientistOffice of Institutional Research, Assessment & Evaluation Purdue University

mdpistilli@purdue.edu | @mdpistilli

March 22, 2014