Embed Size (px)
Citation preview
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
2
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.
4
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)
6
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
7
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
8
WTOnline
Classroom
PeopleSoftLive SIS with UMUC students
PGCC students and
class data
MC-PGCC-UMUC Transfers
UMUC 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?
10
Research Goals• Define outcome variables• Define predictor variables• Model the student lifecycle• Determine the success and failure
factors• Develop and implement interventions• Impact outcomes
11
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)
12
Transfer Student Progressions
13
cc
cc
First Semester
Semester 2 Last Semester
Four-Year Institution
Demog and Other
Academic Work
Transfer
Transfer
Graduate School
Research Studies
14
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 DisciplinesFirst 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
26
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
27
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
28
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
29
Predictors of Success
Race
Gender
Math
Computer
On-line
Age
Speech
Course Efficiency
English
Success @ UMUC
Marital Status
C.C
. C
ou
rses
Honors
Remedial Logistic Regression
30
Question• What CC variables do you think are
good predictors?
31
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
32
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
33
34
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
35
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
36
Interventions
• Identify areas of risk • Collaborate with CC• Develop intervention strategies
– Advising– Messaging– Learning community– Course development
37
3 Projects Synergizing
Kresge PAR
Civitas
38
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
39
Lessons Learned
• Long term plan for data up front• Get a project manager• Manage expectations• Communicate progress far and
wide
40
