This session will dive into how Ivy Tech, the nation’s largest singly-accredited community college, leveraged AWS to gather, analyze and take action on student behavior data to increase success for over 3,100 students. Ivy Tech’s methodology can be applied to any industry and has been hugely impactful to their business. We will follow the steps from inception to implementation across the state of Indiana and how to leverage AWS to bring new capabilities to bear on complex business problems. Learn More: https://aws.amazon.com/government-education/

1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
How Ivy Tech's
Applied Predictive Analysis Project
Leveraged Data to Help Students Succeed
June 14, 2017
Lige Hensley, Chief Technology Officer, Ivy Tech

2. • Ivy Tech is Indiana’s statewide community college and the largest singly-accredited
two-year college in the country
• 8,300 administrators, staff and faculty educating nearly 170,000 students annually
• Over 130 locations around Indiana with over 60,000 online and in-person course
sections each year
• 1,200 different software applications in use across the college
• Over 100,000 network devices active each day with about 45,000 BYOD devices
• User base of just over 940,000 people
• Generate over 100,000,000 rows of data each day across the enterprise…and almost
all of that is student related in some way
The Biggest School You’ve Never Heard Of

3. The Monster In The Room
Our students were not completing, and
thus persisting, as much as we would
like

4. Weapons of Math Destruction
• Our cloud based data warehouse is super fast and flexible
• With the amount of data we have, this is the only affordable way for us to
tackle big problems
• Open source software allows us to do things at a far lower cost

5. Questions... And More Questions
What if we could tell a student
was in trouble automatically…
• How early could we tell?
• What we would do about it?
• How accurate could we be?
• Where would we even start?

6. So We Asked
What makes a good student
Good support at home
Does homework
Engaged in class
Collaborative

7. What Does This Really Mean?
Engaged in class
• Pays attention
• Participates in class
• Reads assignment
• Does lab work
• Does homework
• Raises their hand
• Enjoys the material

8. How Do We Represent That In Data?
• Participates in class
• Logs into LMS
• Emails classmates or faculty
• Goes to labs, lectures or other activities
• Uses discussion boards
• Does NOT get flagged by advisor or faculty
• Uses chat
• Tweets about class

9. And The Inverse
• Does not participate in class
• Does not log into LMS
• Does not email classmates or faculty
• Does not go to labs, lectures or other activities
• Doesn’t use discussion boards
• Gets flagged by advisor or faculty
• Doesn’t use chat
• Doesn’t Tweet about class

10. And More Specifically
• Participates in class
• LMS login actions
• Meta data from email systems
• Data from activities system, library, campus systems
• LMS and portal logs
• Advising system data
• Chat system logs
• Twitter data

11. Blend
LMS
Logins
Email
Metadata Activity
Data
LMS
Logs
Portal
Logs
Advising
Data
Chat
Metadata
Tweet
Metadata
Machine Learning
Tools

12. Serve
Good Student
Prediction Model

13. Reuse
Good Student
Prediction Model
LMS
Logins
Email
Metadata
Activity
Data
LMS
Logs
Portal
Logs
Advising
Data
Chat
Metadata
Tweet
Metadata
DAILY

14. Presto!

15. And what it’s good for…
 Predicting Student Success
• Identify behaviors of successful and unsuccessful students.
• Quickly determine which path a student is on.
• We can now predict, with over 80% accuracy, which students are likely to fail a
course within the first two weeks of a 16-week term.
• We don’t build models, we built a system that builds models based on student
behavior as of yesterday

16. Questions?

17. Thank you!