From Reporting to Insight to Action:
How Data are Changing Post-secondary Education
Ellen D. Wagner
Chief Strategy Officer, PAR Framework
• 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
• Is predicting risk enough to move the needle on risk
mitigation to improve student success?
• What does this mean for online learning?
Setting the Context:
Data Are Changing Everything
“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….
What is the one thing we don’t do???
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,
What do we want?
The RIGHT Answers!!
When do we want them? NOW!!
Getting to the right answer takes work
• Analysis and model building is an
• Around 70-80% efforts are spent
on data exploration and
SAS Analysis/Modeling Process
Three Opportunities on our Horizons
• Linking predictions to action
• Creating new insights and opportunities with
• Delivering on the promise of what online
learning can be
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.
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
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
• We need to get out of the way of what we’ve been and
embrace where we need to go.
Online Learning as a Catalyst for
Changing Data Sensibilities
Sloan-C Babson, 2013
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
Bloomberg BusinessWeek Research Services Analytics Insights (2014):
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
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
• Engagement with more than 39 forward thinking US institutions
• Small, high functioning team with partner, subject and domain
• In-kind donations to date
▫ IBM Tableau
▫ Blackboard iData
• Total Counts Time Frame
• August 2009 – May 2013
– 13,090,351 course records
– 1,842,917 student records
Creating scalable solutions for institutional
effectiveness and student success through
-common data definitions
Structured, Readily Available Data
• Common data
definitions = reusable
predictive models and
• Openly published via a
cc license @
PAR Core Competencies,
Provide insight into how
institutions compare to
their peers through
common measures by
institutional datasets for
Identify which students
need assistance, by using
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
within and across
Different Levels of Insight
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
4. What were the attributes and
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?
DATA DELIVERY AND QA TOOLS
• Automated response and
self service Q1 2014
• 300 automated tests
• Available now
• Unlimited institutional users
• Released November / May
• Member driven report
• Expanded report sets
• Releasing on tablets/devices
• Dynamic institution
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
• Delivered up to 3x a year year,
within 21 days of data
• Rapid turn model publications
for watch lists
INTERVENTION INVENTORY TOOLS
• Online application built on SSMx
• Lays groundwork for intervention
• 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
Thank you for interest
Ellen D. Wagner, Ph.D.