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Predictive Analytics & Enrollment
ManagementChris J. Foley
Director of Undergraduate Admissions
Mary Beth MyersRegistrar
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Question #1
Can we more accurately predict the size of the incoming freshman class?
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• 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
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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
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The 3 Model ResultsAdmittedFTFT Applied SIGS
21483476 1168 160
27193551 672 160
31323472 180 160May 1st Model
March 1st Model
January 1st Model
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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
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Yields2012
Actual2013
Actual2014
Predicted
Jan 1st Model 44% 44% 41%
Mar 1st Model 44% 44% 42%
May 1st Model 44% 44% 41%
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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%.
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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
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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?
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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
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R 110 Analysis
2013
2012
May 1st PredictedR 110 Residual
8671,248 381
1,4921,960 468
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2014 Projected (estimated)
May 1st PredictedR 110 Residual
1,3111,896 585
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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
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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
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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
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• 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
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• 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