ima 2011 martin spielauer ron anderson

Student success analysis and prediction using the US community

college microsimulation model MicroCC

IMA 2011

Martin SpielauerRon Anderson

This project was funded by the US National Science Foundation's Advanced Technological Education (ATE) Program with a grant to Colorado University's DECA Project

Organization• Context & Goals• Why Microsimulation• MicroCC

– General– Data– Behaviours

• Simulations results & Illustrations– Overall fit & trends– Compositional analysis: outline– Compositional analysis: examples

• Discussion & Outlook

2Spielauer & Anderson

Context & Goals• Enhanced understanding of US Community College (CC)

student success pathways

• Many initiatives to improve completion success (< 40%)• Initiatives triggered data collection / utilization• Challenges

– Heterogeneity of programs– Heterogeneity of students– Demographic & economic change– Success hard to define and to compare

• Microsimulation can complement statistical analysis


Why Microsimulation• Education research key engine in development of advanced

statistical methods, e.g. multilevel models• Individual level study progression data available• Microsimulation can complement statistical analysis

– Quantify individual level differences; decomposition– Projections accounting for composition effects– Policy analysis– Momentum point analysis– Capacity planning– Data development

• Education part of most large scale MS models; underused in education research


MicroCC: Overview• MicroCC (Micro-Community-College) is a proof of concept

model– Simple but able to reproduce observed totals, pattern

and trends– Based on real data– Output to demonstrate power and flexibility of MS

• Proved useful as demonstrational tool– Development and discussion of research proposals– Potential partners and clients – Data providers

• Used to assess data quality and needs


MicroCC: Data• Rhode Island: 2500 students per study cohort 2005+• Connecticut: 200.000 students, cohorts 2000+• Three populations:

– Rhode Island 2005– Connecticut: “Advanced Technical programs” (ATE)– Connecticut: Non-technical studies

• Variables– Demographic: age (group), sex– Race: (Non Latin) White, Black, Latin, Asian, Other– Term by term: Number of courses enrolled and passed


MicroCC: Model• Synthetic starting population sampled from the initial

distribution of students by province/program, cohort, age group, sex, race, and full-/part-time status

• Students followed over 4.5 years (9 terms)• Four decisions per term

– (Re-)enrolment decision– Fulltime / part-time decision– Number of courses enrolled (1-3; 4-10)– Courses passed

• Models estimated separately by sex and province/program: 42 logistic (& ordered logit) models

• Success: 12 courses passed (proxy for transfer-readiness)


MicroCC: Technical implementation• Implemented in the generic microsimulation language

Modgen developed and maintained at Statistics Canada


Illustration: Overall fit and trend


0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

2000

-Fal

l

Sum

mer

2001

-Fal

l

Sum

mer

2002

-Fal

l

Sum

mer

2003

-Fal

l

Sum

mer

2004

-Fal

l

Sum

mer

2005

-Fal

l

Sum

mer

2006

-Fal

l

Sum

mer

2007

-Fal

l

Sum

mer

2008

-Fal

l

Sum

mer

2009

-Fal

l

Sum

mer

NON-ATE DATA

NON-ATE SIMULATION

ATE DATA

ATE SIMULATION

Modeled and observed trends in Connecticut succes rates

Illustration: Decomposition – Intro 1/4


-5.0%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

Compositional analysis: Latin students compared to White students, RI

Effect of different course success probability

Effect of different number of courses enrolled

Effect of different probability to continue/switch to fulltime

Effect of different re-enrolment probability

Success Rate of Latin Students

Success Rate of White Students



-5.0%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%










-5.0%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%








Difference due to different population composition at first enrolment

Illustration: Rhode Island, Latin vs. White


-10.0%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

Male Female Initially fulltime

Initially part-time

Age at enrolment

<22

Age at enrolment

22+

Total

Decomposition of differences in study success rates between Latin and White students - RI 2005 cohort - main groups






Success rate of White students

Illustration: Rhode Island, Black vs. White


-10.0%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%


Initially part-time

Age at enrolment

<22

Age at enrolment

22+

Total

Decomposition of differences in study success rates between Black and White students - RI 2005 cohort - main groups





Success Rate of Black Students


Illustration: Connecticut, Black vs. White


-10.0%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%


Initially part-time

Age at enrolment

<22

Age at enrolment

22+

Total

Decomposition of differences in study success rates between Black and White students - CT-TECH 2005 cohort - main groups





Success Rate of Black Students


Illustration: Connecticut, ATE vs. non-ATE


0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

Initially fulltime student

Initially parttime student

Male Female Age at enrolment

<22

Age at enrolment

22+

All

Effect of different course success

Effect of different number of courses enroled

Effect of different probability to switch to / continue fulltimeFulltime-Parttime

Effect of different re-enrollment probability

NON-TECHNICAL

TECHNICAL (ATE)

Decomposition of different study success rates between technical (ATE) and non-technical students in Connecticut

Outlook• Organizational: New England Board of Higher Education

– Coordinating center, project management, training– Development of projects & proposals / funding

• Planned enhancements & projects for college institutions in New England– Job Market and Transfer Success. A college conducts an

annual follow-up survey– Evaluation of a Campus-Wide Intervention– Enrollment forecasting and capacity planning on state

level


ima 2011 martin spielauer ron anderson

Documents

spielauer anderson5microcc

decomposition intro

trend9spielauer anderson

martin spielauerron

outlinecompositional

data quality

courses passedmodels

clients data providersused