KENNETH URBAN

Student Persistence and Drive Time

Overview

The purposeWhat did the literature say?How were data collected?What data were collected?What was related to persistence?Did it work?What about distance?

The purpose

Find out how factors related to persistence in the literature compared to what internal experts thought

Predict which students were likely to persist, using data collected on the registration form

Use the prediction to provide services to at-risk students

What did the literature say?

The “Big Nine” persistence risks for students at two-year public institutions like MSTC: Being 24 years old or older Delaying postsecondary education by more than 1

year after high school Enrolling less than full time Being independent Working full-time Being currently or previously married Being a single parent Having dependents Not having a regular high school diploma

How were data collected?

Survey to two sets of internal experts Front-line staff Administrative staff

Mined from PeopleSoft Data available from the registration form only Six-semester cohorts: 1036, 1042, 1046 Business division associate-degree students (except

Business Management) All three campuses

What data were collected?

RaceGender

Age

Delay

DistanceAlternative HS

Diploma

FinanceWork

Single Parent

Academic PreparationDisabilityEnrollment Lead

Time

Persistence

Standalone VariablesStandalone Variables SME accuracy?SME accuracy?

Race (0.122, p<0.015) Gender (0.120, p<0.016) Enrollment Lead (0.169,

p<0.001) Age Delay Distance GED/HSED Finances Work Single Parent Academic prep Disability

Good Poor Good Good Good Mixed Poor Poor Poor Poor Mixed Mixed

What was related to persistence?

Did it work?

Process Divided the student data in half Used one half to work statistical magic Used the statistics to predict persistence in the other

half

Best variables, working together Race (0.5133¹, 1.67²) Gender (0.7414, 2.10) Single-parent (-0.3469, 0.71) Declared disability (0.6060, 1.83)¹Coefficient²Odds ratio

Did it work?

Performance

Persistence type

InSample

CorrectlyClassified

Incorrectly

Classified

Persister(%)

89(44.5%)

45(50.6%)

44(49.4%)

Non-persister(%)

111(55.5%)

71(64.0%)

40(36.0%)

Total 200 116(58.0%)

84(42.0%)

Recommendations

Important variables from the literature not collected Add first-gen, parental postsecondary experience,

marital status, dependents, enrollment intensity, and student intent

Missing data in PeopleSoft Repeat with newer data

Data from a single division used Include students from other divisions

What about distance?

No relation to persistenceInteresting patterns emerged

Accurate placement of students Variations in driving patterns by campus

What’s a drive time zone?Microsoft MapPoint software

What about distance?

Questions

Make-up by Source District

District MF WR SP Total By Dist

Adams-Friendship 2 12 2 16 1.96%

Almond-Bancroft - 4 6 10 1.22%

Amherst - 4 10 14 1.71%

Auburndale 10 8 1 19 2.32%

Granton 7 - - 7 0.86%

Marshfield 86 14 - 100 12.22%

Nekoosa 1 41 4 46 5.62%

Pittsville 6 11 - 17 2.08%

Port Edwards** 1 12 - 13 1.59%

Stevens Point 8 35 208 251 30.68%

Tri-County - 7 8 15 1.83%

Wisconsin Rapids** 2 193 18 213 26.04%

Within MSTC district 123 341 257 721 88.14%

Outside MSTC district 38 19 40 97 11.86%

Total 161 360 297 818

76.40% 94.72% 86.53%