Breaking the Iron Triangle
Sustaining Excellence in the New Financial Reality
University Systems Forum
The Iron Triangle
3
Challenges Across All Components of Mission
•
•
•
Increasing gap between high and low income
Falling behind other countries
Generational decline in % with a degree
Access
Affordability
•
•
•
Quality
•
•
•
Rising tuition
Growing financial need
Increasing debt
Stagnant retention and graduation rates
Questions about learning outcomes
Employer complaints about skills
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It’s Going to Get Worse Before It Gets Better
4
Economic and Demographic Trends Will Increase the Pressure
•
•
Shift to outcomes-based
funding
Increased transparency
and accountability
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•
•
Increasing competition
from out-of-state
Merit aid arms race for
top students
•
•
Black
76%
Asian
-2%
82%
Hispanic
-4%
White
2027
2024
2021
2018
2015
2012
2009
2006
2003
2000
1997
2012
2010
Projected Growth in Georgia HS
Graduates, 2010-2020
2008
Annual Change in Georgia High
School Graduates
2006
Georgia Educational Appropriations
per FTE (Constant $)
2004
All Growth in Non-Traditional
Student Populations
2002
Slower Growth in Overall
18-22 Year Old Population
2000
Continued Decline in State
Funding per Student
Rising student support
costs
Higher risk student
populations
Source: SHEEO, WICHE
No Easy Answers
5
Common Proposals Require Unacceptable Tradeoffs
Affordability
Access
Quality
Cap student tuition
and fees
Encourage disruptive
innovation
Raise admissions
standards
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The Limits of Cost-Cutting Initiatives
6
Necessary But Not Sufficient
Biggest Cost Drivers
Difficult to Cut
Major E&E Audits Bring
in 2-3% of OpEx, Once
Costs Creep
Back After Cuts
Financial
Sustainability
Your Institution
Large Cuts Hurt
Morale, Quality
Faculty Won’t
Tolerate Large Cuts
Low-Hanging Fruit
Already Gone
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Notes:
7
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Managing Tradeoffs at the Micro Level
8
Using Data to Align Investments with Outcomes
Lessons Learned Across EAB Studies
Collect data on costs
and outcomes at the
lowest level possible
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Support front line
decision-makers in
using data to make
tradeoffs
Create incentives for
decision-makers to
share in institutional
improvement
Change policies and
structures to remove
roadblocks
Finding New Opportunities to Improve
9
“Murky Middle” Represents Big Chance to Improve Success Rates
Histogram of All Students by First-Year GPA
SSC Georgia Data Set
9K
8K
Number of Students
7K
6K
87% return for
The Murky Middle
in Georgia
Graduates within 6 Years
Continued Enrollees Past 6 Years
a second year
(+3% SSC avg)
2nd to 6th Year Departures
35% graduate
1st Year Departures
within six years
(-13% SSC avg)
5K
4K
3K
2K
1K
0K
First-Year GPA
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The “Coordinated Care Network”
10
Building a Continuously Improving Student Support Infrastructure
1
Advisors use risk analytics and alerts to identify
and triage struggling students, refer them to
appropriate support service, and collect results
2
Administrators view utilization reports and
outcomes data to assess support service
effectiveness and make continuous improvements
Predictive Models
Academic Support
Student Referral Network
Student
check-ins
Career Advising
Case
Referrals
Proactive
Campaigns
Financial Aid
Effectiveness
Feedback Loop
Advisors
Leadership
Student Services
Impact
analyses
Systemic
Improvement
Tutoring
More precise
identification of risk
Better targeted
advice and support
3
Comprehensive
Student Risk Data
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Greater return on
retention investments
Institutions and EAB partner to improve risk identification, drive
systemic change and elevate the impact of the entire system
Proactive Outreach
and Interventions
Campus-Wide
Case Management
Central Reporting
and Evaluation
Source: EAB interviews and analysis
Notes:
11
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What Would a “Predictive” Campus Look Like?
Proactive, Preventative Services Customized to Individual Students
Early Warning
Admissions
Financial Aid
Systems would automatically
sense at-risk students and
notify intervention teams,
without relying on faculty alerts
Admissions would identify which
applicants have the best chance
of graduating – and which need
help right from the start
Financial Aid would anticipate
warning signs of financial
distress and deploy targeted
assistance at key moments
Advising
Academic
Programs
Support
Services
Advisors would guide students
based on proven patterns of
success, customized to their
individual needs and goals
Students would pick majors
based not just on interest, but
also on likelihood of graduation
and career success
Staff would precisely target
customized services to students
– before they even know that
they need help
Source: Education Advisory Board interviews and analysis.
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12
Managing in an Environment of Scarcity
13
The Disconnect Between Decisions and Consequences
Teaching
Research
Service
Much time
released for
low-value
activities
Level of
Investment
The Reality of
Excess
Course releases
granted without
regard for
performance
Proliferation of
niche courses,
independent
studies, tracks
Not enough
instructors to
open bottlenecks
Not enough
faculty/support to
increase research
output
The Perception
of Scarcity
Demands of
admin work
greater than
ever
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Bringing Transparency to Academic Decision-Making
14
EAB’s Pilot Research Project
A Year-Long Joint Effort Answers Three Key Questions
1
2
3
Is it possible to extract and standardize
data at the department level?
Is it possible to benchmark across
institutions?
Are there real departmental and
institutional opportunities?
Yes
EAB Engineers integrate Finance,
Human Resources, Student and
Financial Aid data
Yes
EAB Implementation Analysts apply
the same key financial and quality
metric definitions within and across
institutions
Yes
the results identify new and
substantive opportunities for
improved academic -resource
allocation
EAB Analysts standardize disparate
data definitions to ensure data
consistency
Definition of Section Fill Rates
Sampling of Opportunity Areas
Curricular Complexity
Payroll
Courses
Institution A
Faculty
Catalog
Programs
Ledger
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Institution B
Section enrollment at
beginning of semester
Section enrollment
after add/drop period
Maximum section
capacity
Maximum section
capacity
Over- and Under-Filled
Sections
Faculty Administrative Workload
Program Prioritization
Finding and Reallocating Academic Resources
15
A Roadmap for Realizing Academic Ambitions
Space
Utilization
Course
Offerings
Course
Success
§ Identify course
access
bottlenecks
§ Consolidate
underutilized
sections
§ Expand
bottleneck
courses
§ Rationalize major
curricula
§ Maximize capacity
utilization
§ Better leverage
existing space
§ Reduce number
of small courses
§ Limit high-DFW
courses
§ Defuse inefficient
gen ed reform
§ Differentiate
faculty workloads
50%
33%
20%
30%
60%
Classroom
Utilization
Underutilized
Sections
Attempted Credits
Not Completed
Curricular
Focus
Faculty
Workload
Students
Graduating with
Excess Credits
Faculty Teaching
Less than
Standard Load
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Space Utilization
Instructional Space Utilization
16
Benchmarks from Ad Astra’s Higher Education Scheduling Index
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Enrollment Underutilized Balanced
Ratio
Courses
Courses
USG (n = 5)
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Overloaded First Year Classroom
Courses Overloaded Utilization
Courses
(Standard
Week)
AASCU (n = 22)
4 yr Publics (n = 53)
Classroom
Utilization
(Primetime
Week)
Course Offerings
An Easy Win
17
Significant Gains by Consolidating Sections Within a Single Course
Lower Division Anthropology Course
Section 1
Section 3
69%
Enrollment – 31, Maximum - 45
56%
Section 2
+
Enrollment - 25, Maximum - 45
Sections 1-3
67%
Enrollment - 30, Maximum - 45
Section 4
38%
=
Enrollment - 17, Maximum - 45
Collapsing Sections
Assuming Optimal Fill Rate
of 80%
289
25%
Sections taught
by adjuncts
75%
Superfluous sections
Sections taught
by full-time faculty
80%
Enrollment – 36, Maximum - 45
200
$330,000
Adjunct credit hour
savings
Savings from
adjunct
875
$1.5M
Full-time faculty
credit hour savings
Long-term savings
from faculty
1) For analyses, all courses with a maximum enrollment of zero are excluded.
Source: Education Advisory Board, Gates Research Project.
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Course Success
A Clear Opportunity for Improvement
18
High DFW Variability Within a Course Demands Further Analysis
Failure Rates Vary Drastically, Even Within a Single Course
DFW Rates by Section and by Course, Fall 2013, Public Master’s University
100%
95%
90%
73%
69%
97%
97%
72%
58%
100%
64%
47%
Acc201
Bio101
Psy200
“The greatest (financial) impact we can make at our institution is by focusing our
attention on improving retention in our lower division courses.”
Chief Business Officer
Public Flagship Research Institution
1) All sections in graphic have a minimum of 19 students.
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Source: Education Advisory Board, Gates Research Project.
Curricular Focus
The Rococo Curriculum
19
Course Diversity Increasing Faster Than Enrollment
Are We Neglecting Bottlenecks in Favor of Curricular Diversity?
Increase in Enrollment, Sections, and Courses, 2009-2013, Public Master’s Univ.
SCH Delivered
Substantial increase in student
demand…
7.1%
Sections Offered
3.2%
Distinct Courses Offered
… But an even greater rise in
new courses offered
12.5%
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Source: EAB interviews and analysis
Faculty Workload
Is It “Standard” If No One’s Doing It?
Large Share of Faculty Time Released or Unaccounted For
Overwhelming Majority of Faculty
Don’t Work Standard Load…
Share of Faculty by Load Status1, Public
Master’s University
The Primary Reasons for “Underloading”
Research Releases
Service/Admin Releases
62%
Insufficient Demand
16%
Underload
Standard Load
23%
Alternative Compensation
Overload
Who’s Minding the Shop?
... Especially at Research Institutions?
57%
Share of FT faculty teaching
capacity utilized (Representative
Department, Public Research
Institution)
“There is a black market on campus for
overload, supplemental pay, and reduced
loads – no one has any data on this.”
Vice Provost
Public Master’s University
1) Standard load is 24 semester credit hours
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Source: EAB interviews and analysis
20
What’s Standing in Our Way?
Roadblocks to Data-Driven Academic Resource Management
Creating a Usable System of Metrics
21
Changing the Decision-Making Process
•
No consensus on how to measure most
important objectives
•
Resources traditionally allocated based on
seniority, relationships, and institutional politics
•
Current data collection methods make it
difficult to answer basic questions
•
Tendency to use data to support pre-existing
decisions rather than identify new options
•
Many interrelated factors driving performance
make responsible inputs difficult to decipher
•
Academics often skeptical of receiving benefits
from data-driven efficiency gains
•
Variety of institutions complicate to defining
comparison groups
•
Difficult to reallocate specialized resources
from low demand to high demand areas
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Notes:
22
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