This document summarizes a presentation about how data and predictive analytics are changing expectations around accountability and student success in post-secondary education. It discusses how predictive modeling can identify at-risk students, but predicting risk is not enough - institutions must also link predictions to interventions. The presentation also explores opportunities to generate new insights from data and leverage online learning to fulfill its promise by embracing analytics. The PAR Framework collaborative aims to help institutions improve effectiveness and outcomes through common data definitions, benchmarks, predictive models, and an intervention inventory tool.

1. From Reporting to Insight to Action:
How Data are Changing Post-secondary Education
Ellen D. Wagner
Chief Strategy Officer, PAR Framework

2. Session Overview
• This session explores changing data sensibilities at US post-
secondary institutions. Particular attention is paid to how
predictive analytics are changing expectations for
institutional accountability and student success.
• Results from current work in postsecondary education show
that predictive modeling can effectively identify students at
risk.
• Is predicting risk enough to move the needle on risk
mitigation to improve student success?
• What does this mean for online learning?

3. Setting the Context:
Data Are Changing Everything

4. “Meh… education researchers have
always worked with data.”
• We do qualitative research with data
• We do quantitative research with data
• We do evaluations with data
• We develop surveys and instruments and experiments to
collect more data
• We pull data from LMSs, SISs, ERPs, CRMs …
• We write reports, summaries, make presentations, develop
articles and books and webcasts….

5. What is the one thing we don’t do???
Data
mining

6. Data Optimize Online Experience
The “digital breadcrumbs” that online technology users leave
about viewing, engagement and behaviors, interests
preferences provide massive amounts of information that
can be mined to better optimize online experience.
It’s about convenience, personalization, recommendations,
just-in-time, just-the-right-device.

7. What do we want?
The RIGHT Answers!!
When do we want them? NOW!!

8. Getting to the right answer takes work
• Analysis and model building is an
iterative process
• Around 70-80% efforts are spent
on data exploration and
understanding.
SAS Analysis/Modeling Process

9. Three Opportunities on our Horizons
• Linking predictions to action
• Creating new insights and opportunities with
data
• Delivering on the promise of what online
learning can be

10. Link Predictions to Action
• Predictive analytics refer to a wide varieties of methodologies.
There is no single “best” way of doing predictives. You need
to know what you are looking for.
• Simply knowing who is at risk is simply not enough.
Predictions have value when they are tied to what you can
do about it.
• Linking behavioral predictions of risk with interventions at
the best points of fit offers a powerful strategy for increasing
rates of student retention, academic progress and completion.

11. Create new insights and opportunities
for data in our practices
• Enrollment management
• Student services
• Program and learning experience design
• Content creation
• Retention, completion
• Gainful employment
• Institutional Culture

12. Delivering on the promises
of what Online Learning can be
• Online learning is more than MOOCs, but that has now
become the public perception of what we do.
• We are watching our our practice disaggregated into 3rd party
platforms and apps.
• We find ourselves at the center of strategic conversations, but
the ways in which we are evaluated continue to miss the
mark.
• We need to get out of the way of what we’ve been and
embrace where we need to go.

13. Online Learning as a Catalyst for
Changing Data Sensibilities
Sloan-C Babson, 2013

14. And yet……
Sloan-C Babson, 2013

15. Faculty still don’t trust it
Sloan-C Babson, 2013

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17. How Are We Doing So Far?
• Data analytics are still emerging. Many organization still rely
on traditional technology (e.g. spreadsheets) and methods
(e.g. inferential statistics).
• Analytics tend to be used narrowly within departments and
business units, not integrated across the institutions.
• Intuition based on experience is still the driving factor in data-
driven decision-making. Analytics are used as a part of the
process.
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Bloomberg BusinessWeek Research Services Analytics Insights (2014):

18. How Are We Doing So Far?
• Data is the number 1 challenge in the adoption and use of
analytics. Organization continue to struggle with data
accuracy, consistency, access.
• Analytics to solve big issues, with the primary focus on
reducing costs, improving the bottom line, managing risk.
• Many organizations lack the proper analytical talent.
Organizations that struggle with making good use of analytics
often don’t know how to apply the results.
• Culture plays a critical role in the effective use of data
analytics.
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19. Collaborative
National
Multi-institutional
Non-profit
Institutional Effectiveness
+
Student Success

20. About PAR Framework
• Established, growing non-profit collaborative focused on using
existing institutional data to improve institutional effectiveness
and student outcomes
• Funded by Bill & Melinda Gates Foundation 2011, 2012, 2013
• Managed at the Western Interstate Commission for Higher
Education
• Engagement with more than 39 forward thinking US institutions
• Small, high functioning team with partner, subject and domain
expertise
• In-kind donations to date
▫ IBM Tableau
▫ Blackboard iData
▫ Starfish

21. DATA STATISTICS
• Total Counts Time Frame
• August 2009 – May 2013
– 13,090,351 course records
– 1,842,917 student records

22. PAR Objectives
Creating scalable solutions for institutional
effectiveness and student success through
-common data definitions
-common measures
-institutional collaboration

23. Structured, Readily Available Data
• Common data
definitions = reusable
predictive models and
meaningful
comparisons.
• Openly published via a
cc license @
https://public.datacook
book.com/public/institu
tions/par

24. Speak the
same
language

25. PAR Core Competencies,
helping members
IDENTIFY -
Benchmarks
Provide insight into how
institutions compare to
their peers through
common measures by
scaling multi-
institutional datasets for
benchmarking and
research purposes.
TARGET -
Models
Identify which students
need assistance, by using
in-depth, institutional
specific predictive models
Models are unique to the
needs and priorities of our
member institutions based
on their specific data.
Determine the best way
to address areas of
weakness identified in
benchmarks and models
by scaling and leveraging
a member and literature
validated framework for
examining interventions
within and across
institutions (SSMx)
TREAT
Interventions

26. Different Levels of Insight
Cross Institutional
Student/degree/major level
insight into:
1. What did the retention look
like for students entering in
the same cohort
2. How does your institution
compare to peer institutions /
institutions in other sectors
3. What was the relationship of
student attributes
4. What were the attributes and
performance outcomes
Institutional Specific insight into:
1. What students are being
retained over time?
2. Which students are currently at
risk for completing and why?
3. Which factors are directly
correlated to student success?
PAR Benchmarks
Descriptive Analytics
PAR Models
Predictive Analytics

27. DATA DELIVERY AND QA TOOLS
• Automated response and
self service Q1 2014
• 300 automated tests

28. BENCHMARKS
• Available now
• Unlimited institutional users
• Released November / May
• Member driven report
development
• Expanded report sets
• Releasing on tablets/devices
• Dynamic institution

29. INSTITUTIONAL PREDICTIVE MODELS
• Delivered a a limited beta
Member driven institutional
model targets selection
• Migrating SAS Visual Analytics
delivery with next data set
• Unlimited number of
institutional logins
• Delivered up to 3x a year year,
within 21 days of data
acceptance
• Rapid turn model publications
for watch lists

30. INTERVENTION INVENTORY TOOLS
• Online application built on SSMx
• Lays groundwork for intervention
benchmarks
• Enables institutional snapshot on
expanded intervention dataset
• Ver. 1 launches Q1 ‘14
• Ver. 1.1 with reporting Q3 ‘14
• Designed for integration with
benchmarks and models
• Full integration of reporting Q3
‘14

31. Thank you for interest
Ellen D. Wagner, Ph.D.
edwsonoma@gmail.com
ellen.wagner@parframework.org
http://.parframework.org
http://twitter.com/edwsonoma
+1.415.613.2690 mobile
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