Predictive Analytics & Enrollment

ManagementChris J. Foley

Director of Undergraduate Admissions

Mary Beth MyersRegistrar

Question #1

Can we more accurately predict the size of the incoming freshman class?

• Traditional yield ratios cannot take into consideration shifts in the composition of the applicant pool• Given the rate that IUPUI is attracting different

types of students, incorrect predictions are likely based on yield ratios• Models based on regression analysis may

provide a solution

Predictive Modeling

AdmittedFTFT Applied SIGS

Summer, International,

and Gen Studies Students

2012 = 1672013 = 162

2014 = 160 est.

Ratio between the non-

decisioned apps to enrolled

students not in Admitted or

SIGS

Regression equation based on multiple data

points

First-time full-time cohort as

reported by UIRR

The 3 Model ResultsAdmittedFTFT Applied SIGS

21483476 1168 160

27193551 672 160

31323472 180 160May 1st Model

March 1st Model

January 1st Model

Significant VariablesJanuary

Academic HonorsApp btwn Oct-NovClass RankClass SizeClass SizeDistance from campusEthnicityGPAHigh School Home CountyMax SAT or ACT scoreNetwork ID CreatedPlan/MajorProgram/SchoolRank PercentageReferral Source CodeRegion of Home AddressTop 10 Rank

MarchAcademic HonorsApp btwn Dec-JanApp btwn Oct-NovClass SizeCore 40Distance from home to campusEthnicity is knownFirst GenerationGenderGPAGraduation PeriodHome StateMax SAT or ACT scoreNbr days applied before term startNetwork ID CreatedProgram CodeRank NumberRank PercentageReferral Source CodeResidencySchool IDSchool StateTop 10 Rank

MayAcademic Honors Max SAT or ACT score

Age When AppliedNbr days applied before term start

App btwn April-MayNbr days from app to admit

App btwn August-Sept Program CodeApp btwn Dec-Jan Rank NumberApp btwn Feb-Mar Rank PercentageApp btwn Oct-Nov Region of the U.S.Application Date ResidencyBirthdate School IDClass Size School NameCore 40 School StateDistance from home to campus School ZIP

First GenerationStudent is Spring HS Graduate

Gender Top 10 RankGPAGraduation PeriodHigh School Out of StateHome CountryHome CountyHome StateHS Grad Period is within 6mos of term

Yields2012

Actual2013

Actual2014

Predicted

Jan 1st Model 44% 44% 41%

Mar 1st Model 44% 44% 42%

May 1st Model 44% 44% 41%

Therefore, the models predict a drop of yield of 2-3 percentage points.

However, our admit-to-deposit yield has shown no decline over prior years

and has actually increased by .5%.

How Did The Models Perform?Prior Ratio Estimates 3,650

Model 1 (Jan 1st) 3,476Model 2 (Mar 1st) 3,557Model 3 (May 1st) 3,472

Actual Enrollment: 3,584

Question #2

Can we predict the number of freshmen who will require COMM R110

in their first 2 semesters based on information available in May?

Predictive Modeling for R 110

Deposited by May 1R 110 Yet to Deposit

Estimate of R 110 enrollees who had not deposited by

May 1st

Regression equation based on multiple data

points of May 1st Deposits

Number of new freshmen who enrolled in R

110 in either fall or spring semester

R 110 Analysis

2013

2012

May 1st PredictedR 110 Residual

8671,248 381

1,4921,960 468

2014 Projected (estimated)

May 1st PredictedR 110 Residual

1,3111,896 585

Significant Variables for R 110 ModelPositive (Increased Likelihood of

Enrolling in R 110)Negative (Decreased Likelihood of

Enrolling in R 1110)

Business First Generation

Technology Science

Avon HS Address Pre-Medicine Program

Mooresville HS Pre-Music Technology

May/June Graduate Pre-Nursing

Pre-Computer Science Ben Davis University HS

Pre-Mechanical Engineering Franklin Community HS

Greenfield Central HS

Biology BS major

Pre-Herron Fine Arts

Course Enrollment

Max Enroll Requested

Actual Enrollment

% Fill

Priority Registration 1210 233 19.261st Day of Classes 2006 1943 96.86Census 1650 1889 114.48

Fall 2013 COMM-R 100 Enrollment

Of the 1889 enrolled at census: 1,102 (71%) were first year UG

Course Enrollment

Max Enroll Requested

Actual Enrollment

% Fill

Priority Registration 1690 249 14.731st Day of Classes 2387 2391 100.17Census 1962 2355 120.03

Fall 2014 COMM-R 100 Enrollment

Enrolled 466 more students than Fall 2013

Of the 2355 enrolled at census: 1,398 (68%) were first year UG

• Analyze success of fall 2014 freshman model• Analyze spring 2015 R 110 course data once

available• Complete overall course analysis for fall 2014 &

spring 2015 based on fall 2014 freshman model (R 110 & W 131)

Next Steps

• Build enrollment models for fall 2015 freshmen• Build and test W 131 May 1st model• Build and test May 15th model• Explore the use of individual probability scores

for recruitment• Analyze R 110 and W 131 course data based on

best fall 2015 model including significant variables

Next Steps