Ron Anderson rea@umn.edu

This project was funded by the National Science Foundation ATE Program for Targeted Research. The grant was to Colorado University’s DECA Project, Liesel Ritchie, PI, with a subcontract to Rainbow Research for Project I, Strategies for Improving Recruitment, Retention and Placement.

October 27, 2011

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Community College completion rates embarrassing low at 20 to 40% within 8 years.

Advanced Technology Programs (ATP), while not as bad as non-ATP programs, still lose over 50% of their students before completion.

Gender inequality, a serious problem in NSF ATE projects

Recruitment of racial minorities improving in NSF ATE projects.

NSF ATE projects neglect student advising & other strategies to retain students

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*Data from Program Improvement Projects in Western Michigan State annual ATE Survey by www.evalu-ate.org

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Data from Program Improvement Projects in Western Michigan State annual ATE Survey: www.evalu-ate.org

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Data from Program Improvement Projects in Western Michigan State annual ATE Survey: www.evalu-ate.org

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Advanced Technology Programs (ATP) fail toAttract Women. Data graphed are First-term

Enrollments by Gender for ATP & Non-ATP

Data are based on all students enrolled in Connecticut Community Colleges 1999-2009.(N=120,000)

Many organizations are trying to address the completion/success gap in 2-year colleges

Analytics movement attempting to forecast student dropouts

Whitehouse Committee on Measures of Student Success ◦ Appointed in 2010◦ Sept. 2011 interim report◦ April, 2012 target for preliminary report◦ Years before impact likely

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Common Completion Metrics (National Governors Assoc.)

Voluntary Framework of Accountability (AAAC) Foundations of Excellence in the First College

Year (Gardner Institute) Complete College America Achieve, Inc (35 State network) Achieving the Dream (Database and Dashboards) Western Interstate Commission for Higher

Education (WICHE) – Human Capital Database Project Gates Foundation - funded analytics initiatives National Agenda for Analytics (EDUCAUSE)

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Predictive Analytics (Capella U & others) Data Analytics (Sinclair Community College) Incisive Analytics (IncisiveAnalytics.com) Platinum Analytics (AstraSchedule.com) Action Analytics (Symposia in 2009 & 2010,

and EDUCAUSE in 2011) Learning Analytics (1st International Conference on

Learning Analytics, Feb. 27, 2011)

Student Success Analytics (Purdue U., etc.)

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Analytics is sometime used as synonymous with ‘analysis’ to sound impressive.

More precisely, ‘analytics’ refers to ‘predictive analytics,’ or analysis of trend data to predict future events of individuals or populations.

Current analytics does not follow individual course-taking histories across time, thus it is weak in providing individualized information that students can use.

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Typical Analytics Data:Trend Line, not a Trajectory

(Trend lines fail to give any information about changein individual attributes overtime, only aggregates.)

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Cohorts Showing Student Trajectoriesfor 120,000 student histories in Conn.

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Student-Pathway Trajectories showing Race Gaps

Data are all 2,407 students first enrolled Fall, 2005 in the Community College of Rhode Island system. Completion is defined as graduation, articulation, or completion of 48+ credits within 7 terms (4.5 years).

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Recent, dynamic microsimulation techniques make it possible to follow individual course-taking histories (trajectories) across time

Thus, using student transcript data records, models can be built that simulate student enrollment decisions term by term..

The results give information that students and student advisors can use to greatly improve their chances of completing a program successfully.

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Microsimulation model developed in Modgen programming language from Statistics Canada

Hundreds of thousands of student transcript records from the CCs of Connecticut and Rhode Island were used as test data sets.

For any given set of data, each scenario simulation is repeated for an equivalent sample of 5 million students to eliminate random variability, which only takes about 2-3 minutes.

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MicroCC developed with Targeted Research funds from NSF ATE program.

Initial model includes 4 student choices or behaviors (details on next slide)

Model’s core (predictive factors) are derived from data at hand◦ 28 separate logistic (and ordered logit) regression

models run to calculate coefficients for each factor and interaction that predicts success or completion

Multiple scenarios can be simulated by modifying either ◦ starting populations (mostly demographic factors)

Gender, race, age, and initial full-/part-time status effect coefficients for student decisions, or

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1) Enrollment/re-enrollment choice in each term

2) Full vs Part Time enrollment in each term

3) Number of courses attempted

4) Successful completion of each course attempted

Process Decision Points: MicroCC Completes this Decision Sequence for each term of each Student

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Success = completion of program (graduate, certificate, successful transfer, or completion of a required number of courses)

Total courses completed = completion of 12 or more courses within 10 terms (5 years)

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Momentum Point One Passed - student completed 3 courses in first term

Momentum Point Two Passed - student completed 6 courses in year one

Stopout - student temporarily does not enroll in term X

Stopouts -total terms student stopped out

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Used in MicroCC◦ Gender (M/F)◦ Race (W/B/L/O)◦ Age (to 21/22+)◦ Starting term enrollment full-time vs part-time

Data not available in 2010 for MicroCC model◦ Financial aid in term X◦ Concurrent job ◦ Marital status◦ Prior postsecondary education

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Data Restructuring – Creation of longitudinal file from term-level files can be done but it is time consuming.

Missing Data – Records on transfer status, graduations, and certificate completions may be incomplete or nonexistent.

Summer Term Challenge – can summer credits be ignored completely because there are so few regular students enroll in summer terms, or should credits and courses completed during the summer, be added into the counts for the previous term?

Developmental Courses -- Developmental courses were tracked but institutions handled them differently.

Transfer credits -- Are they added to new credits, and if so, when?

Simultaneous enrollments -- In Connecticut we found many students enrolled in multiple colleges during a single term.

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Screen print from MicroCC with Student Success Model for Baseline scenario with RI and CT data

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◦ Data for MicroCC microsimulations came from two State enrollment databases: Rhode Island Community College – 5 annual cohorts

with most analysis just on the 2,502 students first enrolled in Fall 2005 for 4.5 years

Connecticut Community College system – 276,469 students in 10 cohorts beginning Fall 1999 to 2009.

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Screen print from MicroCC with Student Pathways Models for Baseline scenario with RI and CT data

Sample output table for student success rates by term

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Gaps in success can be deconstructed, identifying the student pathways that created specific portions of the gap.

These results have direct relevance for students and guidance counselors, toward improving success rates.

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1) Enrollment/re-enrollment choice in each term

2) Full vs Part Time enrollment in each term

3) Number of courses attempted

4) Successful completion of each course attempted

Process Decision Points: MicroCC Completes this Decision Sequence for each term of each Student

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Most (90%) CT students in ATPs were in engineering and manufacturing programs. The remainder were in IT, network, and misc. science and technology programs.

The 7,310 ATP enrollees in CT were only 6% of all CC students.

As shown in the next chart, ATP students has a 17% higher completion rate than non-ATP students.

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Source: 7,310 ATE Students in Connecticut CCs 2000-2009

The amount of impact they have on success depends upon specific regions, schools, and curricular programs.

If a student enrolls full time plus works full time and has children to raise, s/he might not do well in coursework and thus not keep up the momentum toward completion.

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But both students and their advisors need to understand how crucial these decisions are to pathway success:

1. To enroll continuously – no stop outs 2. To enroll full time 3. To take the larger numbers of courses each

term, within reason 4. To pass the courses attempted.

The simulation model incorporates these decisions, not just at first enrollment, but at every term in which the student is enrolled.

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Remaining charts from microsimulations illustrate how student decisions influence different subgroups of students within ATP programs in CT.

Example 1, shows elements of gap between CT and ATP White and Hispanic men

Example 2, highlights the higher completion rates of women over men in CT ATPs

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Source: 7,310 ATE Students in Connecticut CCs 2000-2009

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Women Outpace Men in all Race Categories - Percent of Students Completing their Programs

by Gender & by Race in Conn. N=7,310 ATE students

Microsimulation can uncover enrollment decisions that have huge effects on student success.

These student decisions can sometimes explain demographic differences.

Adding additional data, e.g., job history, financial aid and retention interventions, e.g., mentoring, as factors in the models, can make the methodology even more powerful.

Enrollment forecasting can be done with greater precision. The model could also be extended to include post-schooling

job trajectories as well.

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For More information contact Ron Anderson rea@umn.edu or 952-473-5910

1. The ATE program should invest in student tracking data systems, either in conjunction with existing student record systems or, better yet, a separate data system to which ATE-funded projects had to contribute.

2. ATE-funded projects should be encouraged or required to address and report on student advising practices.

3. Training should be developed for high school and community college student advisors regarding the needs of STEM students

4. Recruitment of women (with improved advising) into STEM pathways needs to be given greater priority

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NSF ATE projects may be neglecting student advising & related strategies to retain students.

Of the 305 projects and centers recently funded by the NSF ATE program, only two mentioned “student advising” or “guidance counseling” in their title or abstract. However, 10 projects (1%) mentioned “counselors.”

ATE projects could utilize the findings of MicroCC simulations as guides for student advising. A system for student progress coaching and advising is needed with every ATE funded project

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Microsimulations should be run on many more States, college populations, and ATE program populations, so that findings could be tailored to specific groups of at-risk students.

Input data for simulations should be expanded to include job status, financial aid, and other items relevant to student success.

Microsimulation should be extended to include articulation and job acquisition processes.

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