upcea new england 2013 - non-traditional student retention model creation

Square Peg in a Round Hole:Developing Student Retention Models in Programs Designed for Adult Learners

Rachael DenisonDirector – Enrollment Research, Strategy, & Data

Management

Matthew HendricksonAssociate Director – Strategic Enrollment Research

October 24, 2013

Agenda

• Intro• NU & CPS• Context• Retention definition• Reports• Analysis of Indicators• Findings: W/I’s & Interventions• Next Steps

About Northeastern – a top tier private research university

Northeastern University• 7 colleges, 2 schools• 16,385 full-time UG students• 4,202 full-time GR students• Signature co-op program• Boston, Charlotte, Seattle, Online

College of Professional Studies (CPS)• Certificate Doctoral degrees• Faculty of scholar-practitioners and industry

professionals; practitioner-based degree programs• 67 degrees offered online• 11,000 undergrad, grad, English language and

international pathway program students

Need• Mission-driven• Predict & stabilize

enrollments• Financial impact• Focused recruitment

strategies• Focused advising &

student support services

Challenges• Standard measures

don’t fit• Stop outs vs. drop outs• Work, family impacts• Application predictors

limited (no standard test scores)

Our square peg/round hole predicament

SM1

SM2

FL1

FL2

WN1

WN2

SP1

SP2

Fiscal Year

FL1

FL2

SP1

SP2

Fiscal Year

Retention Defined

Return

Graduate

SuccessFY

Cohort

Combined UG Report (Masked Data)Initial Year 2nd Year 3rd YearCohort Start

SizeReturn % Grad % Success % Return % Grad % Success%

FY05 150 50% 5% 55% 30% 5% 35%FY06 200 55% 5% 60% 35% 10% 45%FY07 300 60% 5% 65% 40% 15% 55%FY08 500 65% 5% 70% 40% 20% 60%FY09 550 70% 5% 75% 45% 20% 65%FY10 850 65% 10% 75%FY11 800

Combined UG Report (Masked Data)Initial Year 2nd Year 3rd YearCohort Start

SizeReturn % Grad % Success % Return % Grad % Success%

FY05 150 50% 5% 55% 30% 5% 35%FY06 200 55% 5% 60% 35% 10% 45%FY07 300 60% 5% 65% 40% 15% 55%FY08 500 65% 5% 70% 40% 20% 60%FY09 550 70% 5% 75% 45% 20% 65%FY10 850 65% 10% 75%FY11 800

Combined UG Report (Masked Data)Initial Year 2nd Year 3rd YearCohort Start

SizeReturn % Grad % Success % Return % Grad % Success%

FY05 150 50% 5% 55% 30% 5% 35%FY06 200 55% 5% 60% 35% 10% 45%FY07 300 60% 5% 65% 40% 15% 55%FY08 500 65% 5% 70% 40% 20% 60%FY09 550 70% 5% 75% 45% 20% 65%FY10 850 65% 10% 75%FY11 800

Predictive Analytics

• Goal = Predict Success

• Sample– Fiscal Years: 2009, 2010, 2011– Graduate Students

• Analytical Methods– Decision Trees– Neural Networks– Regression

Data Types• Application:

– Employment history– Military service

• Enrollment:– First Term:

• GPA• Withdrawal or

Incomplete

– Special programs

• Demographic:– Age– Ethnicity– Location– Gender

• Financial Aid:– Application– Granted– Type– Amount

Data Types• Application:

– Employment history– Military service

• Enrollment:– First Term:

• GPA• Withdrawal or

Incomplete

– Special programs

• Demographic:– Age– Ethnicity– Location– Gender

• Financial Aid:– Application– Granted– Type– Amount

Initial Findings

• Surprised of weak predictive value– Data inconsistencies– Small samples– Financial aid– Demographic information

• Good outcome – Withdrawal or Incomplete– Small sample, but large impact– Early alert– Student outreach

Next Steps

Further build out of predictive analytics• Overlay admissions,

LMS, early alert, call center data

Monitor other measures of retention• Within terms• Term 1 to term 2

Implement informed retention strategies• Measure impact of

strategies

Questions & Thank You

r.denison@neu.eduRachael Denison

m.hendrickson@neu.eduMatthew Hendrickson