Student success analysis and prediction using the US community college microsimulation model MicroCC IMA 2011 Martin Spielauer Ron 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 3Spielauer & Anderson

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 4Spielauer & Anderson

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 5Spielauer & Anderson

MicroCC: Data Rhode Island: 2500 students per study cohort Connecticut: students, cohorts 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 6Spielauer & Anderson

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) 7Spielauer & Anderson

MicroCC: Technical implementation Implemented in the generic microsimulation language Modgen developed and maintained at Statistics Canada 8Spielauer & Anderson

Illustration: Overall fit and trend 9Spielauer & Anderson

Illustration: Decomposition – Intro 1/4 10Spielauer & Anderson

Illustration: Decomposition – Intro 2/4 11Spielauer & Anderson

Illustration: Decomposition – Intro 3/4 12Spielauer & Anderson

Illustration: Decomposition – Intro 4/4 13Spielauer & Anderson

Illustration: Rhode Island, Latin vs. White 14Spielauer & Anderson

Illustration: Rhode Island, Black vs. White 15Spielauer & Anderson

Illustration: Connecticut, Black vs. White 16Spielauer & Anderson

Illustration: Connecticut, ATE vs. non-ATE 17Spielauer & Anderson

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 18Spielauer & Anderson