CIO Enrollment
Management Presentation
April 17, 2015
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What Do We Need to Be
Effective Enrollment
Managers?
Today’s Objectives
Identify gaps in our enrollment
strategy
Predict FTES With a Support
System
Managing Low and High
Enrollment Courses
Identify Course Taking Patterns
Enrollment Management
Survey
We Asked A Few Simple Questions:
• 30 Respondents
Answers:
• We all need new technologies
• Nobody is really there yet
What else do you want/need?
 Managing student pathways,
particularly in Gen Ed areas.
 Tools for managing class sizes that
support the Completion Agenda within
current funding formulas
 Making EM changes without contention
 Multi-year planning tool
 Practical strategies for maintaining
productivity and increasing enrollment
 What does a comprehensive
enrollment management plan look
like?
 It should include elements of attendance
accounting, successful outreach efforts, and
management strategies.
Scheduling software that integrates
facilities use
Best practices in scheduling of
accelerated courses for student
completion
Predicting FTES
with a Decision
Support System
Bob Hughes
Director, Enterprise
Application Systems
Mt. San Antonio College
We have the
data;
the problem is
timeliness
Timeliness
 Plenty of reports showing how we did at the end of the
term esp. CCFS-320
 Plenty of reports showing how we are doing during the
term with regard to FTES
 Not enough actionable data during registration - the
critical period when we can meet students needs (and
capture more FTES) by meeting demand before classes
start
A Decision Support System
 Custom built in-house using Oracle Application Express
 No additional license fee (covered by our existing Oracle
license)
 Resides in the same database as Banner
 Built by a researcher temporarily re-assigned as a
programmer
 Received the Excellence in College Planning Award in
2015 from the RP group
Three Horizons
1. What can we do in the current term to better
meet student demand?
2. What can we do in the upcoming term to better
meet student demand (during registration)?
3. What can we do next year to better meet
student demand (schedule build)?
Horizon 1:
The Current Term
Each Dean gets a view specific to her division. The landing page shows a graph containing all
classes in the division in four categories of fill rates.
Clicking on a color of the graph brings you to a list of sections in that category.
This example shows the 45 classes in the 60% - 74.99% range.
Built in actions allow you to add or remove columns, add additional filters, and download to Excel
5 standard report formats are available
An example of the FTES report by Department. Shows Current FTES vs Projected FTES (4 year
weighted average) vs Potential FTES (all sections at 100%)
Similar comparison between Divisions
Horizon 2:
The Upcoming Term
What are our ‘hottest’ classes? This report shows classes with fill rates of 90% or more,
ordered by how quickly they filled. All 47 sections of ENGL 1C filled in the first 4 days of registration.
What demand are we missing? After the classes are full and waitlists are full, we can measure
intensity of demand by looking at attempts to register after the class has closed.
What classes are lagging or exceeding in their demand? This report shows a comparison of
fill rates by day during registration vs prior years and projected fill rates (weighted average).
Philosophy is meeting projections.
Sociology is lagging both historical demand and projected fill rates.
Horizon 3:
Next Year
FTES Distribution for the year by Division.
FTES Distribution for the term by Division.
FTES Distribution for the year by Departments in a single Division. Do we need to change the offering
pattern (for example – more offerings in Fall vs Spring?)
FTES Distribution for each term by Departments in a single Division.
Sandbox – a place to experiment with course scheduling decisions before the schedule is built.
Start from a baseline of what was offered last year.
This section of AMLA 21S didn’t fill as expected. We know why – we expect it to do better next year.
We can increase this to the projected fill rate (4 year weighted average), or enter our own prediction.
Default is projected fill rate. We’ll enter 85%.
User updated courses now show at the top of the list
We’re going to add some additional sections. We choose courses where the demand is the greatest.
One option is where there was most demand after all sections of the course filled.
Second option is where all sections of the course filled quickest during registration.
We added another section of PHIL 15.
We can also add brand new courses, or courses that are beyond the ‘top three’ in terms of demand.
Note that this is listed as ‘User Created’. We can delete these if necessary; the schedule will revert
back to the historical schedule.
Three changes resulted in a projected increase of 8.3 FTES. We can do several models. Each user
has their own “sandbox” to simulate schedules.
The Result?
Fall 2013 – demand began to soften
Used the Decision Support System during
registration for Spring 2014 to add/cancel classes
where warranted, and identify areas where more
marketing was needed
Met FTES targets in 2013-14
Mandate to grow 3% from Funded FTES in 20142015
Currently on target to be up 4% for the year
Enrollment and Efficiency
Santanu Bandyopadhyay
Cypress College
Nuts & Bolts: The Data Points
Trend information
 Enrollment/FTES by
division
 Fill rate by division
 WSCH/FTES by division
 Unmet demand
(unduplicated) by Course
 Demand/Supply analysis
by course level
 Growth opportunities
Previous semester
 Classroom availability
 # Full-time Faculty by
department
 Class size
 Deficit in extended day
budget by division
Demand Analysis – Trend
 Unmet demand (unduplicated) by course: # Unduplicated
students who attempted to register for a section PLUS #
waitlisted students
After adding 8 sections (240 seats), demand for SOC 101 declined by 213 in 2015.
For Math 101, after adding 4 sections (120 seats), demand increased by 18.
Implications for future planning? Increasing v. flat demand?
Demand Analysis – Trend
• Demand/Supply analysis by course level: # seats offered v.
seats occupied for levels of courses
Availability of seats and waitlisted students in each level indicate potential for
better alignment between demand and supply
At 201 to 250 level, nearly 1,200 seats are available although 648 students are
waitlisted
If perfect alignment is achieved, demand will exceed supply for only 100 level
classes
Demand v. Contribution
Four Quadrants
 Green (Q1): High demand,
High capacity
 Amber (top left) (Q2): High
demand, low capacity
 Red (Q3): Low demand, low
capacity
 Amber (bottom right) (Q4):
Low demand, high capacity
Using 4-Quadrant Grid
Focus on Q1 and Q2 courses for growth
Caveat: Q2 courses may increase deficit in
extended day
Q3 are likely 3rd and 4th sem classes or new
programs: needed for completion and/or
transfer. Schedule in alternate semester/year
Potential to grow: Q4 – Outreach Plan?
Tying it all together
Demand
• Focus on Unmet Demand
• Student need upper level classes
Capacity
• Evaluate Physical Capacity
• Balance online growth with on-campus
Efficiency
• Build capacity by improving efficiency
• Factor in cost: 4-quadrant guide
Schedule Analysis and
Marketing for Growth
Craig Justice and Kathi Swanson
Student-centered Scheduling
Student choices as consumers
focus on day of week, time of day,
cost, work schedules,
transportation issues among
others
ACE Survey
American Council on Education
(ACE) survey indicate that the
main forecasting tools used for
scheduling are historical course
enrollment information and local
knowledge rather than indicators
of future student demand
Newer Tools
Predictive Analytics
Wait List Data
Student Education Plan Data
Social Media Survey Tools
Strategic Marketing
Capstones, Math & English
Predictive Analytics
Wait List Data
Student Education Plan Data
Social Media Survey Tools
Section Cancellation Analysis
Section Cancellation Analysis
Curriculum Analysis
Capstones
Sequences
Bottlenecks
Curriculum Analysis
Using Data and Tools to Plan
The Challenge: Less Nordstrom Style
Scheduling, More Student-Centered
Scheduling
Shifting the Course Scheduling Culture
Use good analytical data to inform the plan
Plan the Schedule First, Staff It Second
Marketing: Inform the Customers
Completion Dashboards
Integrating with Degree Audit to Change Student
Behavior and Predict Course Needs
JoAnna Schilling, Cerritos College
JoAnna Schilling,
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Frank Mixson,
Frank Mixson,
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Frank Mixson,
Frank Mixson,
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Debra Moore,
Discussion