Data speaks. Discover how Ivy Tech, the nation's largest singly accredited community college, uses AWS to gather, analyze, and take action on student behavioral data for the betterment of over 3,100 students. This session outlines the process from inception to implementation across the state of Indiana and highlights how Ivy Tech's model can be applied to your own complex business problems.

1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Lige Hensley
C h i e f T e c h n o l o g y O f f i c e r , I v y T e c h C o m m u n i t y C o l l e g e o f I n d i a n a
T a k i n g a P a g e O u t o f I v y T e c h ’ s B o o k : U s i n g D a t a f o r S t u d e n t
S u c c e s s
N o v e m b e r 2 8 , 2 0 1 7
Building a Superpower

2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
The biggest school you’ve never heard of
• 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 more than 60,000 online and in-person course sections
each year
• 1,200 software applications in use across the college
• Over 100,000 network devices active each day, with about 45,000 BYODs
• User base of slightly more than 727,000 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

3. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
The business problem
Our students were not
persisting, and thus completing,
as much as we would like

4. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Our tools
Our cloud-based data warehouse is fast and flexible
With the amount of data we have, the cloud 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. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Questions…and more questions
What if we could automatically tell
a student was in trouble…
• How early could we tell?
• What would we do about it?
• How accurate could we be?
• Where would we start?

6. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
So we asked one more
question…

7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
What makes a good student
Good support at home
Does homework
Engaged in class
Collaborative

8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Engaged in class
• Pays attention
• Participates in class
• Reads assignment
• Does lab work
• Does homework
• Raises their hand
• Enjoys the material
What does this really mean?

9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
How do we represent this 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

10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
The inverse is helpful, too
• Participates 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

11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
And more specifically
• Participates in class
• LMS login actions
• Metadata from email systems
• Data from activities system, library, campus systems
• LMS and portal logs
• Advising system data
• Chat system logs
• Twitter metadata

12. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
High-level flow
Cloud databases/
SaaS sources
AirFlow
Amazon Glacier
Amazon Redshift
Amazon S3
ERP
Ivy Tech AI
Slackbot
controller

13. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Full refresh DAG

14. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Two types of features
• Natural
• Feature that is aggregated, summed, averaged, or otherwise pulled out of
the data we have
• Examples: logins in the past 24 hours, 7 days, month
• Synthetic
• Feature that is a function of other data, designed to represent some
aspect of what we are modeling
• Example: how hard are you working?

15. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Blend features
LMS
logins
Email
metadata Activity
data
LMS
logs
Portal
logs
Advising
data
Chat
metadata
Tweet
metadata
Machine learning
tools

16. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Serve
Good student
prediction model

17. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
What is it 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
• Fall 2016 more than 16,000 students identified and contacted
• By end of Fall 2016, over 3,100 more students were passing classes

18. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Redshift tuning tidbits
 To save money, we implement dev/test/production using execution
queues
 We analyze and vacuum tables once a week to keep performance at an
acceptable level
 Distribution and sort key tuning on large tables is critical to good
performance of our AI queries
 Tweaking distribution and sort key settings can have negative effects on
other queries, so thorough testing is essential

19. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Costs summary
 30 TB used
 $3,200 per month
 That’s 95% cheaper than a “traditional" approach
 This system helped us identify more than 4,000 previously unrealized
credentials earned by students

20. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Thank you!
l i g e @ i v y t e c h . e d u