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?