Strategic Plan for Enrollment
Management Institutional
Facilities & Capacity
Management Subgroup
Task Force Presentation
September 28, 2010
Agenda
1. Charge
2. Membership & Process
3. Preliminary Results
a) Quantitative Model for Resource Demands
b) Qualitative Changes to Increase Capacity
4. Questions and Discussion
Charge to the Committee
Goal: Develop facility and capacity management
model to support the enrollment target of
25,400 students and credit hour production of
625,000.
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Identify the ideal number of tenure track faculty
Identify ideal faculty/student ratios
Identify ideal academic advising/student ratios
Integrate accreditation requirements
Identify appropriate utilization of technology to
improve operations
Committee Tasks
1.
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Establish baseline ratios for capacity
management & minimum requirements to
support institutional enrollment goals,
addressing:
Classroom capacity
Teaching delivery methodologies (distance vs. faceto-face; on- vs. off-campus)
Number of tenure-track faculty
Course Loads
Course fill rates
Housing capacity
Computer labs
Recreational facilities
Committee Tasks
2.
Establish strategic objectives (including
related strategies/ programs and measurable
outcomes) that operationalize the facility and
capacity management goals.
Membership & Process
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Committee Membership
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Helen Brantley, Chair, TLRN
Brian Brim, Provost’s Office
James Erman, Interim VPR (Spring)
Phil Eubanks, Chair, ENGL
Lisa Freeman, VPR (Fall)
Lori Marcellus, Dir. UG Studies, CoB
Cheryl Ross, Finance & Facilities
Mike Stang, Dir., Housing & Dining
Matt Venaas, Student Trustee (Spring)
Kelly Wesener, Student Affairs
Objective #1: Quantitative Model
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The goal is to forecast what future student
populations might look like, and to project the
resources needed to appropriately serve
those populations
Most resource demands depend on who the
student is: major, academic level, level of
ability, etc. This requires a detailed student
profile.
We need to differentiate between enrollmentdriven resources and student-driven
resources.
Quantitative Model
Enrollment-Driven
Resources
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Professorial Faculty
Instructors
Adjunct & Clinical
Faculty
Graduate Assistants
Graders & Tutors
Classrooms
Laboratories
Computer labs
Student-Driven
Resources
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Advising
Housing & Dining
Counseling
CAAR
Career Services
Health Services
Quantitative Model
“Student profile” means student headcount
parsed (at present) by
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Declared major/emphasis
Academic level
Point of entry (transfer vs. new freshman)
GPA (for this analysis, simply tracked whether the
GPA was above or below 2.0)
For now, focus of quantitative model is on
on-campus undergraduate population.
WARNING!
This model has a number of limitations:
• It is based on data from our recent past, and
relies on only 4 semesters of data
• That data has a lot of “turbulence” resulting
from the transition into MyNIU
• It assumes that patterns of student behavior
are constant
• It can only capture trends that have occurred
in our recent past
• It neglects many small effects to focus on the
larger trends
USE WITH CARE!
Quantitative Model
Current
Student
Profile
Future
Student
Profile
Course
Enrollments
Student Driven
Resources
Enrollment Driven
Resources
Quantitative Model
Current
Student
Profile
Future
Student
Profile
Course
Enrollments
Student Driven
Resources
Enrollment Driven
Resources
Quantitative Model
Three kinds of change were modeled in trying to
forecast changes in the student profile:
• Changing patterns of demand for majors
• Changing recruitment patterns (assumed that
recruiting is not targeted at specific majors)
• Changing retention patterns (assumed that
retention strategies apply equally to all
majors): 2% reduction in new freshmen with
GPA less than 2.0; 1% reduction in attrition.
Quantitative Model
Departing
Students
New
Students
Fall 08
Spring 09
Continuing Students
Returning
Students
New
Students
Fall 09
New
Students
Continuing
Students
Departing
Students
Fall to Fall
Student
Behavior
Quantitative Model
Data from Spring 09, Fall 09, Spring 10 and Fall 10 was evaluated. Data gave headcounts by term,
college, degree, major, emphasis, academic level, GPA, admit type, new vs. return.
• The data set did not preserve the point of entry (transfer or native) for students who entered the
university prior to FY 09. Estimates of their transfer/native split were made, and it was assumed
that these distributed evenly across disciplines.
• Components of the student profile with negligible headcounts (e.g. Returning students with no
GPA recorded) were filtered from the data set.
• This produced 19 cohorts (10 small ranging from 44 to 325 students; 9 large ranging from 818 to
2641 students)
• For each cohort, we looked at the % distribution across majors, and measured Spring to Spring
and Fall to Fall changes in that distribution. An ongoing change was forecast for a given major only
if both the F-F and S-S changes were greater than 1 standard deviation in magnitude, and both
pointed in the same direction.
• For those majors, we assumed that there would be additional growth/loss in “market share”
equal to ½ the recent growth/loss.
• This was converted back to absolute growth/loss in headcount, and trimmed to record only those
with an absolute change of 10 or more students.
• Separately, data from Fall 08 was examined to determine fall-to-fall transitions between the 19
cohorts. Fall-to-spring data was used to substitute for unavailable spring-to–fall data.
• This didn’t accurately predict Fall 2010 data, so correction factors were introduced to make the
2010 data work out correctly. These same correction factors were carried forward to 2015.
Quantitative Model
• Changes in demand were modeled by looking
at fall-to-fall and spring-to-spring changes and
making cautious extrapolations.
• To model growth, we took the new recruiting
numbers as inputs, and build a model from
2008 data to capture current retention rates.
• New retention rates were then introduced to
capture the desired improvements, and the
results out to Fall 2015 were forecast.
Enrollment Forecasts
Forecasts for Headcount by GPA level and Admit Type
20000
18000
16000
14000
TU - Above
12000
TU - Below
TU - New
10000
NU - Above
8000
NU - Below
NU - New
6000
4000
2000
0
Fall 2009
Fall 2010
Fall 2011
Fall 2012
Fall 2013
Fall 2014
Fall 2015
Enrollment Forecasts
Forecasts for Headcount by College
7000
6000
5000
Students
CBUS
CEDU
4000
CEET
CHHS
3000
CLAS
CVPA
NIUDK
2000
1000
0
2009
2010
2011
2012
2013
2014
2015
Quantitative Model
Changing Demand Patterns
w/out Growth
Growth and Changing
Demand Patterns
Absolute
%
Fall Absolute
%
Change Change 2015 Change Change
164 16%
1268 220
21%
Major
Nursing
Fall
2009
1048
Forecast
1212
Health Sciences
424
609
185 44%
640
216
51%
Electrical Engineering
260
337
77 30%
358
98
38%
Business Administration
691
721
30
4%
788
97
14%
Kinesiology
247
305
58 23%
323
76
31%
OMIS
130
193
63 48%
201
71
55%
Psychology
828
843
15
897
69
8%
Management
308
255
-53 -17%
270
-38
-12%
History
459
400
-59 -13%
418
-41
-9%
Elementary Education
879
758
-121 -14%
807
-72
-8%
Finance
414
314
-100 -24%
336
-78
-19%
Marketing
540
391
-149 -28%
399
-141
-26%
2%
Quantitative Model
Current
Student
Profile
Future
Student
Profile
Future
Course
Enrollments
Student Driven
Resources
Enrollment Driven
Resources
Quantitative Model
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Obtained matrices of course demand by
student type. Each cell gives the % of
students of a given profile who enroll in a
given course.
Applied this to those majors that are likely
to experience the greatest change, to
obtain an estimate of the impact of those
changes on course enrollments.
Quantitative Model
Course Fall
No.
2009
PSYC
305
119
PHHE
206
169
PSYC
324
122
UNIV
101
343
CHEM
110
269
ENGL
103
332
NURS
301
136
Fall
2010
Fall Absolute
2015 Growth
137
191
54
183
230
47
113
165
43
335
386
43
286
327
41
310
371
39
129
174
38
Course
No.
AHCD
318
AHRS
327
PSYC
245
PSYC
316
PSYC
332
PSYC
300
BIOS
357
Fall
2009
Fall
Fall Absolute
2010 2015 Growth
133
146
183
37
87
125
162
37
39
74
35
126
204
238
34
118
130
163
33
113
142
174
32
95
104
135
31
Quantitative Model
Current
Student
Profile
Future
Student
Profile
Course
Enrollments
Student Driven
Resources
Enrollment Driven
Resources
Quantitative Model
These steps are still underway:
• Collecting data from Student Affairs (Housing
& Dining, Counseling, Health Services) and
advising deans (advisors)
• Collecting data from college offices on the
resources required to provide additional seats.
Objective #2: Qualitative Changes
Identify changes to current practice that can
better utilize existing resources. Those
changes need to:
• Allow us to serve more students
• Preserve quality of the educational
experience
• Require little or no new cost
Qualitative Changes
A.
Classroom Utilization
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Availability of classroom space does not appear to be
a limiting factor in on-campus enrollment growth
Should it begin to become a limiting factor, there are
options for improving efficiency in use of existing
space:
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Develop metrics for appropriate room utilization
Identify courses that consistently under-utilize the capacity
of the classrooms assigned
Examine the temporal distribution of courses (days of the
week, time of day) and reallocate courses to different time
slots as needed
Increase use of hybrid courses
Increase central management of classroom assignment
process; use scheduling software to increase efficiency
Classroom Utilization
Fall 2010 Room Utilization
Building
Section
Count
Registration
Count
Avg Reg per
Section
Course
Utilization %
Room
Utilization %
DuSable Hall (DU)
903
23303
26
86.8%
53.5%
Barsema Hall (BH)
Jack Arends Visual
Arts Bldg (AB)
309
8826
29
86.0%
32.6%
276
3972
14
62.3%
29.9%
Graham Hall (GH)
248
4673
19
79.2%
56.7%
Wirtz Hall (WZ)
Psyc-Computer
Science Bldg (PM)
224
7923
35
87.6%
45.6%
218
5181
24
90.1%
45.3%
Reavis Hall (RH)
189
3896
21
92.1%
60.4%
Anderson Hall (AN)
187
3216
17
70.9%
47.6%
Davis Hall (DH)
Engineering Building
(EB)
165
3516
21
71.3%
33.9%
164
3113
19
27.6%
32.5%
Faraday Hall (FR)
154
6023
39
87.8%
62.3%
Qualitative Changes
B. Curriculum Planning & Course Scheduling
Ensuring students can construct a schedule that
leads them to graduation in 4 years (2 years as a
transfer student with AA/AS degree)
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Departments need to evaluate their course
offerings, to ensure that needed courses are
available with sufficient capacity and sufficient
frequency.
Curricular review should address the efficiency
and effectiveness of the degree requirements
Consider development of some metrics of
effectiveness of schedules
Qualitative Changes
C. Off-Campus and Distributed Learning
Off-campus venues will be leading opportunities
for
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New degree programs, including professional
masters degrees and degree completion
programs
Certificate programs and short courses
Additional summer offeringsThe greatest
opportunity for increased summer instruction
is also in the off-campus and distributed
learning venues.
Provost Alden is initiating a review process to
develop a new model for the support of
distributed learning instruction
Qualitative Changes
D.
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Forecasting and Data Management
Increase awareness of different metrics available and
their appropriate uses: headcount, SCH, revenue
generated, …
Strengthen the use of forecasting tools and the timely
dissemination of information with predictive value
Need to strengthen and refine forecasting of student demand
Need to improve coordination of data collection and analysis
Forecasts need to provide actionable information in a timely
manner to all units who have the need and opportunity to
“right-size” their resources
Recommend establishment of a data analyst within the
Division of Academic Affairs
Synchronizing the differing schedules for recruiting &
enrollment; scheduling & staffing courses; hiring
instructors and graduate assistants, etc.
Conclusion
• The quantitative model is nearly complete:
– The model for predicting profiles has been built, and is
assimilating the forecasts from the recruiting and retention
subcommittees
– The student-course matrix is being built for high-demand
student populations.
– The data on student-driven resources is being collected
– The final data on course enrollment-driven resources is
being collected
• The most important qualitative issue is the support for off-campus
and distributed learning. This requires high-level discussion and
decision-making before operational plans can be made.
• Questions and comments?