Multi-Institutional Data Predicting Transfer Student

Success

Denise Nadasen Anna Van Wie

Institutional ResearchUniversity of Maryland

University College

Outcomes for this Session• You will learn about the:

– Goals for this grant and the research project

– Process for integrating a multi-institutional data base

– Research questions, methods, and findings– Lessons learned and next steps

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Goals of the grant• Collaborate with the community colleges • Define research questions and variables• Build a dataset for transfer students• Explore predictor/outcome variables • Predict student success • Report the results at national conferences• Use the results to inform policy and

practice to better serve transfer students3

Collaborative Partners

• UMUC is an online institution that enrolls over 90,000 diverse students each year worldwide

• Prince George’s Community College is located within two miles of UMUC’s Academic Center and enrolls over 37,000 diverse students.

• Montgomery College is located within 10 miles of UMUC’s largest regional center, and enrolls over 35,000 diverse students.

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The Team• PI– President, Provost• Sponsor – Institutional Research• Partners

– Montgomery College and Prince George’s Community College

– Undergraduate retention and data mining specialist– External evaluators

• Researchers:– Cheoleon Lee, Jing Gao, Futoshi Yumoto, Husein Abduhl-

Hamid• Data Mining Specialists

– Stephen Penn, The Two Crows5

The Student Population• Students enrolled at UMUC between

2005 and 2011• PG and MC transfer students

– Direct compare (32,000)– National Student Clearinghouse (12,000)– UMUC records (8,000)

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Merging Multi-Institutional Data• Protect this data! • Balance institutional-specific protocols with

research-based definitions• Address data anomalies• Distinguish student level vs. course level• Define LMS data

– Limits on data extract

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KDM• Integrates student data

– Community College and UMUC SIS– Demographic– Courses – Performance– Classroom behavior (LMS)

• 300 source and derived variables• Gather from disparate sources• One time snapshot

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WTOnline

ClassroomPeopleSoft

Live SIS with UMUC students

PGCC students and

class data

MC-PGCC-UMUC TransfersUMUC students who transferred from MC or PGCC and

were matched in the BASE file

Base ExtractUMUC undergrad students enrolled

between Spring 2005 and Spring 2011

Data WarehouseUMUC students from

PeopleSoft Daily Update

MC students and class

data

WT extract

Classroom activity

Prior Work

derived data for transfer students

Question• What barriers would your

institution face in merging multi-institutional data?

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Research Goals• Define outcome variables• Define predictor variables• Model the student lifecycle• Determine the success and failure

factors• Develop and implement interventions• Impact outcomes

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Outcome Variables• Successful course completion (percent)• First term GPA (dichotomized)• Reenrollment in next term (Y/N)• Retention (12 month window – Y/N)• Student Classification (Slackers,

Splitters, Strivers, and Stars)

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Transfer Student Progressions

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cc

cc

First Semester

Semester 2 Last Semester

Four-Year Institution

Demog and Other

Academic Work

Transfer

Transfer

Graduate School

Research Studies

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Which variables contribute to the prediction of online course success?

The data:• 4,558 new, undergraduate, first bachelor-

degree seeking enrollments in 15 UMUC online gateway courses in Spring 2011.

• Transfer data on students from partner institutions, Montgomery College (MC) and Prince George's Community College (PGCC).

Methodology

• Exploratory factor analysis (EFA) was used to identify key covariates.

• Logistic regression was used to predict course success.

Findings• Total number of transferred credits is the best

predictor of course success– pseudo R2 value around .12

• GPA from transferred credits is the second best predictor of course success – pseudo R2 around .11

• Semester course load contributes less to course success than other covariates.

Findings

• Four of five predictors derived from online student behavior show a strong contribution to successful course completion.

Final Predictive Model

Significant Variables

Total number of transfer credits

Summary of students’ week 0 behavior prior to the first day

GPA from transferred credits

Semester course load

Amount of time since students attended the last institution

Significant Online behaviors

Read a conference note

Entering a class

Created a conference note

Created a response note

Which variables predict retention in an online environment?

• The same data set for the prediction of course success

• Add in retention status from Summer 2011, Fall 2011, and Spring 2012.

Methodology

• Logistic regression

• Preliminary analysis focused on the evaluation of covariates (as identified in the previous analysis) predictors based on the students’ coursework behavior, and course success.

Findings

• The covariates and student behavior variables made less of a contribution to this model than the prediction of course success.

• These results indicate that course success may be a good predictor of retention.

What is the relationship between prior academic coursework and UMUC first

semester gateway course on re-enrollment?

The population:• Students new to UMUC in Fall 2008 to Fall

2010• Took ACCT220, BMGT110, CMIS102,

GVPT170, or PSYC100 in their first semester.

Methodology

• Association algorithm Apriori to determine relationships between courses in previous academic work and re-enrollment rates.

• The algorithm indicates when a certain condition is found another condition can be expected.

Findings

Significant Course Relationships

Community College Course Disciplines First UMUC Course

Math or Business ACCT 220

Math or Business CMIS 102

Business or Science BMGT 110

Science or communication GVPT 170

Math or communication PSYC 100

We cannot assume causality

Current Studies

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Examine CC Courses

• Explore relationship between CC courses and first term GPA

• Identify courses of interest• Developmental Ed sequencing• Successful completion of CC course• Mixing course level and student level

data

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Predicting First Term GPA• What CC variables predict first term

GPA of 2.0 or higher?• Course Efficiency• CC courses

– English, Math, Speech, Computer, Honors, On-line Course, Remedial

• Demographics– Age, Gender, Race, Marital Status, Cohort,

Community College Origin, Terms skipped

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The Population

• 9,063 students from MC and PGCC• Mostly Single, African-American, and

female• Most do not skip terms• Most get A’s and B’s at CC• Most have >2.0 at UMUC• Only PGCC offered online courses

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Predictors of Success

Race

Gender

Math

Computer

On-line

Age

Speech

Course Efficiency

English

Success @ UMUC

Marital Status

C.C

. Cou

rses

Honors

Remedial Logistic Regression

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Question• What CC variables do you think are

good predictors?

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Findings

• Predictive variables:– Age, marital status, and under-represented

minorities have predictive power– Math and Honors courses have positive

effects– Remedial and Online have a negative

effect– Course efficiency has a positive effect

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Predicting Student Clusters

• Dataset includes all PGCC and MC students who transferred

• Student level derived variables• Cluster students based on retention and

first term GPA at UMUC• Predict clusters from prior CC work and

demographic variables

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Strivers Stars

Slackers

Splitters

Success QuadrantsR

eten

tion

Yes

Ret

entio

n N

o

GPA > 2.0GPA < 2.0

Stay Tuned ….

• Data mining continues– So far, Stars appears to have

distinguishing features• Focus on top 50 CC courses and

combinations of courses as predictors• Focus on performance in gateway

courses at UMUC as outcomes

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Summary of Findings

• Positive effects– Transfer credit, prior GPA, math, honors,

course efficiency, online activity, age, marital status

– Course success can predict retention• Negative effects

– remedial, online, minority status

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Interventions

• Identify areas of risk • Collaborate with CC• Develop intervention strategies

– Advising– Messaging– Learning community– Course development

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3 Projects Synergizing

Kresge PAR

Civitas

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Student Success

Next Steps

• Examine course success at the CC• Implement/evaluate interventions• Update KDM with more data• Develop, understand, and explain

predictive models to identify at risk students at the CC

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Lessons Learned

• Long term plan for data up front• Get a project manager• Manage expectations• Communicate progress far and

wide

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Questions

• Anna anna.vanwie@umuc.edu

• Denise Denise.nadasen@umuc.edu

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