strategic plan for enrollment management institutional facilities & capacity management subgroup...
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Strategic Plan for Enrollment Management Institutional
Facilities & Capacity Management Subgroup
Task Force PresentationSeptember 28, 2010
Agenda1. Charge2. Membership & Process3. Preliminary Results
a) Quantitative Model for Resource Demandsb) Qualitative Changes to Increase Capacity
4. Questions and Discussion
Charge to the CommitteeGoal: Develop facility and capacity management
model to support the enrollment target of 25,400 students and credit hour production of 625,000.
– 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 Tasks1. Establish baseline ratios for capacity management
& minimum requirements to support institutional enrollment goals, addressing:
– Classroom capacity– Teaching delivery methodologies (distance vs. face-to-
face; on- vs. off-campus)– Number of tenure-track faculty– Course Loads– Course fill rates– Housing capacity– Computer labs– Recreational facilities
Committee Tasks2. Establish strategic objectives (including
related strategies/ programs and measurable outcomes) that operationalize the facility and capacity management goals.
Membership & Process• Committee Membership– 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• 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 enrollment-driven resources and student-driven resources.
Quantitative Model
Enrollment-Driven Resources
– Professorial Faculty– Instructors– Adjunct & Clinical
Faculty– Graduate Assistants– Graders & Tutors– Classrooms– Laboratories– Computer labs
Student-Driven Resources
– Advising– Housing & Dining– Counseling– CAAR– Career Services– Health Services
Quantitative Model
“Student profile” means student headcount parsed (at present) by
– 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 ModelThree 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 ModelNew Students
New Students
Departing Students
Fall 09
Fall 08 Spring 09
Departing Students
Continuing Students
Continuing Students
Returning Students
New Students
Fall to Fall Student Behavior
Quantitative ModelData 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
Fall 2009 Fall 2010 Fall 2011 Fall 2012 Fall 2013 Fall 2014 Fall 20150
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
TU - AboveTU - BelowTU - NewNU - AboveNU - BelowNU - New
Forecasts for Headcount by GPA level and Admit Type
Enrollment Forecasts
2009 2010 2011 2012 2013 2014 20150
1000
2000
3000
4000
5000
6000
7000
Forecasts for Headcount by College
CBUSCEDUCEETCHHSCLASCVPANIUDKSt
uden
ts
Quantitative Model
MajorFall
2009
Changing Demand Patterns w/out Growth Growth and Changing
Demand Patterns
ForecastAbsoluteChange
%Change
Fall2015
AbsoluteChange
%Change
Nursing 1048 1212 164 16% 1268 220 21%
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 2% 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%
Quantitative Model
Current Student Profile
Future Student Profile
Future Course
Enrollments
Student - Driven
Resources
Enrollment - Driven
Resources
Quantitative Model• 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 No.
Fall 2009
Fall2010
Fall2015
AbsoluteGrowth
Course No.
Fall 2009
Fall2010
Fall2015
AbsoluteGrowth
PSYC 305 119 137 191 54
AHCD 318 133 146 183 37
PHHE 206 169 183 230 47
AHRS 327 87 125 162 37
PSYC 324 122 113 165 43
PSYC 245 39 74 35
UNIV 101 343 335 386 43
PSYC 316 126 204 238 34
CHEM 110 269 286 327 41
PSYC 332 118 130 163 33
ENGL 103 332 310 371 39
PSYC 300 113 142 174 32
NURS 301 136 129 174 38
BIOS 357 95 104 135 31
Quantitative Model
Current Student Profile
Future Student Profile
Course Enrollments
Student - Driven
Resources
Enrollment - Driven
Resources
Quantitative ModelThese 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 ChangesA. Classroom Utilization– 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:
• 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 UtilizationFall 2010 Room Utilization
BuildingSection Count
Registration Count
Avg Reg per Section
Course Utilization %
Room Utilization %
DuSable Hall (DU) 903 23303 26 86.8% 53.5%
Barsema Hall (BH) 309 8826 29 86.0% 32.6%Jack Arends Visual Arts Bldg (AB) 276 3972 14 62.3% 29.9%
Graham Hall (GH) 248 4673 19 79.2% 56.7%
Wirtz Hall (WZ) 224 7923 35 87.6% 45.6%Psyc-Computer Science Bldg (PM) 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) 165 3516 21 71.3% 33.9%Engineering Building (EB) 164 3113 19 27.6% 32.5%
Faraday Hall (FR) 154 6023 39 87.8% 62.3%
Qualitative ChangesB. Curriculum Planning & Course SchedulingEnsuring students can construct a schedule that leads them to graduation in 4 years (2 years as a transfer student with AA/AS degree)– 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 ChangesC. Off-Campus and Distributed LearningOff-campus venues will be leading opportunities for – 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 ChangesD. 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?
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