Panel: Developing and Sustaining Workforce Programs: Lessons We're Learning from ATE Targeted Research

1. Debra D. Bragg, Office of Community College
Research and Leadership, University of Illinois

2. Transfer Baccalaureate
Terminal

3. Transfer
Applied
Baccalaureate
Terminal

4. “…a bachelor’s degree designed to incorporate
applied associate courses and degrees once
considered as ‘terminal’ or non-baccalaureate level
while providing students with the higher-order
thinking skills and advanced technical knowledge
and skills so desired in today’s job market.”
Townsend, Bragg, & Ruud (2008, p. 4)

5.  Julia Panke Makela, Research Specialist &
Project Director
 Collin Ruud, Research Associate
 Stacy Bennett, Graduate Research Associate
http://occrl.illinois.edu/projects/nsf_applied_
baccalaureate

6.  Our targeted-research project aims to:
◦ Identify pathways to baccalaureate degrees in
technician education
◦ Analyze pathway designs, implementation, and
outcomes
◦ Describe how AB degree programs operate and meet
students' and employers' workforce needs
◦ Identify and widely disseminate promising and
exemplary practices

7.  Brief survey to identify established formal
pathways to baccalaureate degrees
 Follow-up survey on identified baccalaureate
degree pathways on curriculum and instruction,
accreditation and evaluation, enrollments and students
served, partnerships with employers and other higher
education institutions, and perceived impacts of ATE.
 Case studies with 7–10 ATE projects and
centers to uncover promising ideas and
proven practices

8.  Contacted all NSF-ATE Principal Investigators (PIs) with
grants awarded between1992 and 2011 (~700 grants)
 Inquired about:
• degrees affiliated with the NSF-ATE project or center
• fields of study
• retention and recruitment of underrepresented
student populations at the baccalaureate-level
• access to student-level data for baccalaureate degrees
 Received 234 responses (36% of the sample)

9.  24% of survey respondents reported associate
degrees affiliated with their ATE project or
center with no established pathway to the
baccalaureate
 Some survey non-participants offered
insights into their decision not to participate:
◦ “Our Civil Engineering Practitioner Degree is an AAS
and therefore is a terminal degree. Our participation
in the survey is probably not warranted.”

10.  Baccalaureate degree pathways affiliated with
ATE projects and centers fit both:
• Traditional transfer patterns of AS or AA degrees
transferring to BS or BA degrees
• Emerging pathways such as applied baccalaureate (AB)
and community college baccalaureate (CCB) degrees
 42% (98 of all respondents) indicated that associate degree
programs had established formal baccalaureate degree
pathways
 20% (47 of all respondents) indicated at least one pathway
began from an applied associate degree

11. Manufacturing and Engineering Technology
Computer and Information Technology
Other
Biotechnology
Energy
Electronics
Environmental Technology
Cyber Security and Forensics
Telecommunications
Nanotechnology
Chemical Technology
Geospatial Technology
Civil and Construction Technology
Multimedia Technology
Transportation Technology
Marine Technology
Agricultural Technology
0% 5% 10% 15% 20% 25% 30% 35%
Percent of Respondents Indicating Baccalaureate Degree Pathways

12.  Analysis of 87 of established degree pathways
• Applied Associate  Technical Baccalaureate (22)
• Applied Associate  Traditional Baccalaureate (32)
• Traditional Associate  Technical Baccalaureate (11)
• Traditional Associate  Traditional Baccalaureate (47)
Degree Examples
Applied Associate AAA, AAS, AAAS, AAT, AET, AT
Traditional Associate AA, AS
Technical Baccalaureate BAA, BAS, BAAS, BAT, BT
Traditional Baccalaureate BA, BS

13.  20 respondents identified the following fields
of study:
• Biotechnology • Manufacturing and
• Chemical technology engineering technology
• Computer and • Marine technology
information technology • Nanotechnology
• Cyber security and • Telecommunications
forensics • Transportation technology
• Electronics
• Energy CCB Defined…
• Environmental Any form of baccalaureate degree awarded by an
institution identified as a community college, technical
technology college, two-year college, two-year or technical branch
campus of a university system, or any other institution
that primarily awards associate degrees.

14. Theoretical and Methodological Frameworks
• Program Quality
Unit • Educational Significance
Influences
• Evidence of
Effectiveness and
Institutional Success
Influences
• Replicability and
External Influences Usefulness to Others
Latucca & Stark (2009), Bragg et al. (2002),
Contextual Influences on Academic Plans Sharing What Works: Exemplary and Promising
Programs Evaluation Criteria

15.  Variety makes baccalaureate pathways in
technician education challenging but
compelling to study
 Many questions:
• How are programs designed?
• What perceived needs are they addressing?
• What features contribute to their effectiveness?
• What do we know about student outcomes?
• What can be learned from one program that can be
adopted or adapted in other settings?

16. • Debra D. Bragg
• Email: dbragg@illinois.edu
• Check out our website:
 OCCRL occrl.illinois.edu
◦ http://occrl.illinois.edu • Participate in our
webinars
◦ PH: 217-244-9390
• Get on our listserv
◦ E-mail: occrl@illinois.edu
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17. BUILDING REFLECTIVE
LEADERSHIP:
RESEARCH INTO PRACTICES ATE LEADERS
USE TO DEVELOP AND MAINTAIN
INDUSTRY-RELEVANT CURRICULUM,
PROGRAMS, & INSTRUCTION
Louise Yarnall, Raymond McGhee, & Joseph Ames

18. Research goals
 Deepen understanding about the industry-CC
collaborative cycle to develop workforce programs
 Analysis framed by research model based on past
research and our findings; use model to:
 Tell rich stories about ATE Center cases
 Describe mechanisms for iteratively translating industry input
into curriculum, programs, and instruction
 Describe mechanisms for sustaining the curriculum, program,
and instruction collaboration with industry over time
 Describe common metrics of program success

19. Research background
 Title: Community College Partnership Models for
Workforce Education Sustainability and Integrated
Instruction
 4-year project, beginning Year 3
 4 ATE Centers/Projects:
 Wind energy, biotechnology, engineering technology,
telecommunications and information technology
 Different stages of engagement with industry in instructional
program development: beginning, mid-life, mature
 6-7 associated colleges
 Case studies

20. Research Team and Advisors
SRI Team and Ames Associates Evaluator and Advisory Panelists
 Louise Yarnall, PI  Nick Smith, Evaluator,
Syracuse University
 Ray McGhee, co-PI  Frances Lawrenz, University of
Minnesota
 Geneva Haertel  Cynthia Wilson, The League for
 Robert Murphy Innovation in the Community College
 Manjari Wijenaike, former
 Carolyn Dornsife ATE Center director
 Joseph Ames, Ames  Steve Wendel, NCME
Assoc.  David Jonassen, University of
Missouri

21. Project Overview
 Partnership sub-study:
 Evolution of relationships between industry and community
college in workforce programs
 Unique stories, common mechanisms to translate industry goals
into instructional programs
 Classroom instruction sub-study:
 Tracing industry and ATE Center influences on instructional
programs
 Characterizing range of workforce education instructional
practices and curricula

22. Research products - Partnership
 Cases of ATE Center activities contributing to life cycle of
collaboration with industry in workforce program
development
 ATE principal investigator activities
 Instructional goals
 Rapid development mechanisms
 Sustainability challenges

23. Research products - Instruction
 Cases of ongoing, classroom-level processes that support
continual instructional updates
 Cases of technician education instruction

24. Peek at findings so far
 Model of industry-community college instructional
partnerships
 Partnership sub-study: Early highlights & starting
cases

25. Model: Findings and Uses
 ATE community members can use this model to
strengthen partnerships:
 Stepping back, seeing “big picture” of your work
 Using the categories in the model to “make sense” of
challenges you face, identify potential opportunities
 Researchers use models to make sense of complex
phenomena across multiple settings
 Models emerge from past empirical research and
theory; they evolve based on current data

26. Model: Strategic Need

27. Model: Formation Processes

28. Model: Partnership Capital

29. Model: Outcomes/Outputs

30. ATE-CC Partnership Conceptual Model
FORMATION PARTNERSHIP OUTCOMES/
PROCESSES CAPITAL OUTPUTS
Establishing trust/norms/comm. Creating partnership capital Sustaining the partnership
(Fusing social & org. capital) (Partnership implementation) (Producing results)
Strategic
Need CC support ATE center Industry Resource Student Classroom/ Workplace
role community Leveraging Faculty
Administrator link Certificate
support for Talking with testing Degrees/certif Prepared
Address ATE leader industry Productive
Historic (student pays) icates offered workers
labor supply presence meetings: PD, new placed
needs Organizing technology,
In region Degrees/certif New courses
work groups standards alignment icates created Employee
with faculty Articulates obtained training
Retrain labor need Establish Instructional
incumbent Marketing/out
reach first agreements around Job materials
External
workers equipment, labs / placement/int development
Resources
Trust-building resources ernships
State & local meetings
Improve
technician funding 1/x Instructional
materials sharing
training
Industry adjuncts
Organizational Partnership
boundary Complexity
maintenance -# organizations
-# sectors
-# states
STAGES: Emergence Transition Maturity Critical Cross Roads

31. Partnership sub-study: Early findings
 Cases
 Uses: ATE community leaders can compare their own
situations to these cases, deriving insights

32. Case 1: Regionally scaling a program
 ATE leader role:
 Facilitate regional industry, educators
 Goal:
 Sequence for multi-college ET program
 Rapid Development Mechanisms:
 Identify core courses that transfer
across local fields (boating & medical
devices)
 Crosswalk industry standards to
courses
 Sustainability Challenges:
 Sustain adults past 1 course

33. Case 2: National dissemination
 ATE leader role:
 Moving national industry materials to
colleges
 Goal:
 Providelow-cost, up-to-date, industry-
made IT materials
 Rapid Development Mechanisms:
 Identify IT platform providers with
materials
 Outreach to educators, pass costs to
students, free training & materials
 Sustainability Challenges:
 Staying current

34. Case 3: Local industry exchange
 ATE leader role:
 Develop instructional materials,
communicating with industry
 Goal:
 Enhance existing industry-college
partnership in biotech
 Rapid Development Mechanisms:
 “SWAT” team capacity
 Division of labor around “safety
training”
 Sustainability Challenges:
 Rust belt economy
 Biotech jobs pay half of old jobs
 Global companies, no local loyalty

35. Case 4: Boot camp to program
 ATE leader role:
 Workforce program development
 Goal:
 Expand boot camp to college program
 Rapid Development Mechanisms:
 DACUM
 Sustainability Challenges:
 Timing market need: VC dry up
 Keeping industry engaged
 Facilitating discussions between
educators/industry
 “shop math” vs. “college math”

36. Next steps
 Partnership Study:
 Follow up interviews with stakeholders
 Development of cases, and possibly other tools
 Instruction Study:
 Interviews to build cases: Describe 2 contrasting
partnerships’ specific classroom instructional goals and
programs
 Classroom data to build cases: Select tech classes
representing different levels of technical content and
different emphases on technical vs. professional skills:
 Instructional practice: Classroom observations and interviews
 Curriculum: Artifacts rated by expert panels

37. Thank you
 louise.yarnall@sri.com

38. Stephen Magura
Kelly N. Robertson
The Evaluation Center
Western Michigan University
Presented at the 2011 National ATE PI Conference
Washington, DC, October 27, 2011
Funded by NSF grant # 0832874

39.  Began 1992
 Funding FY 11 - $64 million by NSF
 Approximately 40 centers & 200
projects
 Encompasses biotechnology,
manufacturing, engineering, energy, IT
 Located in community colleges
nationwide

40. 1. “Producing more science & engineering
technicians to meet workforce demands”
2. “Improving the technical skills & general science,
technology, engineering, & mathematics (STEM)
preparation of these technicians” and
3. “(Of) the educators who prepare them”

42.  Objective 1: Formulate a model for standardized
measurement of outputs pertinent to ATE central goals 1, 2
and 3 that is relevant across different Projects and Centers.
 Objective 2: Determine which outputs individual Project
and Centers are measuring as concrete steps toward
achievement of ATE’s central goals and propose
additional outputs that could feasibly be measured.
 Objective 3: Determine what types of evaluation designs
individual ATE Projects and Centers are employing to
determine impact and propose alternative or improved
evaluation designs.

43.  Promote scientific assessment of
effectiveness
 Application of objective effectiveness
measurement strategies
 Better understanding of variations in
success of grantees
 Return on investment of ATE portfolio to
Congress

44. Objective 1. Existing material on ATE compiled
from four sources:
 Selected ATE Project/Center progress and final
reports solicited by an NSF program official
 Project/Center evaluator reports previously
submitted to the ATE Resource Center
 ATE Project/Center websites
 ATE Projects/Centers described in the ATE
Impact publications (Patton, 2008 a,b).

45. Objectives 2 and 3.
 One ATE Project was analyzed in each of ten
industries and one ATE Center in each of seven
industries.
 The Project and Center chosen within each
industry based on the most information available.
 Purpose was to demonstrate that the proposed
framework is applicable to ATE Projects and
Centers across the range of applicable industries.
 Projects and Centers are anonymous in the report.

47. Post-Secondary Secondary
Completed/ Completed/
Enrolled Retention Enrolled Retention
Graduated Graduated
(1) (2) (2 ÷ 1) (1) (2) (2 ÷ 1)
A. Program
⃞
⃞
⃞
⃞
⃞
⃞
B. Course
⃞
⃞
⃞
⃞
⃞
⃞
C. Internship/
⃞
⃞
⃞
⃞
⃞
⃞
Apprenticeship
D. Dual Program/
⃞
⃞
⃞
⃞
⃞
⃞
Dual Credit
Secondary
Post-Secondary Exposed*
Exposed*
E. Software/
⃞
⃞
Materials

49. Secondary Post-Secondary
Number of Educators who Complete… Elementary Middle High Faculty Industry Professional
Professional Development Workshops
⃞
⃞
⃞
⃞
⃞
⃞
Professional Development Courses
⃞
⃞
⃞
⃞
⃞
⃞
Professional Development
⃞
⃞
⃞
⃞
⃞
⃞
Fellowships/Mentoring
Professional Development
⃞
⃞
⃞
⃞
⃞
⃞
Software/Materials*
Note: *Including hard copy and audio/visual materials for professional development purposes

50. Study
Current Project Current Center
Objectives
Creates simulations that teach the Providing educators with
Description underlying science principles of professional development in
biotechnology & nanotechnology. manufacturing.
Track # of teachers trained &
Pre/post test to assess student
2. Current self-assessment of learning. Plan
achievement in relation to the topics
Outputs the simulations intend to teach.
to start asking teachers about
implementation of learning.
Quality of PD course. Test
3. Recommend teacher skills, changes in
Quality of the simulations.
Outputs classroom practices, & student
learning.
4. Current
Post-training satisfaction
Evaluation Pre-test with repeated post-test.
measures.
Design
5. Recommend Expert panel to assess quality of Pre-test with multiple post test
simulations.
Evaluation Compare student learning with
for PD.
Design cohort receiving standard course.

51.  Common ATE Project and Center outputs can
be specified and potentially aggregated to yield
output statistics for the national ATE program
as a whole.
 The proposed framework, consisting of the
figures and the tables in the report, narrows
down and partly standardizes the types of data
collected across ATE projects and centers.

52.  This standardization can result in meaningful
aggregation of output measures that will make
it possible to better determine program
effectiveness.
 Additional instrumentation must be developed
to assess the quality of STEM educational and
outreach resources and their impact on
students’ and educators’ learning and
behavior.

53.  The evaluation framework is also useful
because it identifies the gaps in
instrumentation more precisely.
 The evaluation framework is very
comprehensive, but all elements are not always
applicable to any individual ATE Project or
Center.
 This inherently quantitative data framework
does not diminish the value of additional
qualitative and narrative data that speak to the
value, merit or worth of ATE programs.

54.  Some aspects of the proposed framework are
outside the scope of any individual ATE grant
and would better be pursued through targeted
research.
 This report is not a final prescription, but may
help frame further discussion of ATE
evaluation.

55. My email:
stephen.magura@wmich.edu
Thanks for your attention!

56. Ron Anderson
rea@umn.edu
October 27, 2011
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.
1

57.  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
2

58. *Data from Program Improvement Projects in
Western Michigan State annual ATE Survey by
www.evalu-ate.org
3

59. Data from Program Improvement Projects
in Western Michigan State annual ATE
Survey: www.evalu-ate.org 4

60. Data from Program Improvement Projects
in Western Michigan State annual ATE
Survey: www.evalu-ate.org
5

61. Advanced Technology Programs (ATP) fail to
Attract 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)
6

62.  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
7

63.  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)
8

64.  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.)
9

65.  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.
10

66. Typical Analytics Data:
Trend Line, not a Trajectory
(Trend lines fail to give any information about change
in individual attributes overtime, only aggregates.)
Percent of Students Completing Program X
in each year, 2003-2008
100
90
80
70
60
50
40 46
30 40 39
37 37
20
10
0
2003 2004 2005 2006 2007
11

67. Cohorts Showing Student Trajectories
for 120,000 student histories in Conn.
12

68. 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).
13

69.  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.

14

70.  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.
MicroCC developed with Targeted
Research funds from NSF ATE program.
15

71.  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
16

72. Process Decision Points: MicroCC Completes this
Decision Sequence for each term of each Student
1) Enrollment 3) Number of
/re-enrollment courses
choice in each attempted
term
2) Full vs Part Time 4) Successful
enrollment in completion
each term of each course
attempted
17

73.  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)
18

74.  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
19

75.  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
20

76.  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.
21

77. Screen print from MicroCC with Student Success Model for
Baseline scenario with RI and CT data
22

78. ◦ 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.
23

79. 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
Sample chart of growth of student
completions from above table
0.15
% completed
0.1
0.05
0
1 2 3 4 5 6
terms 1 to 6
24

80. 25

81.  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.
26

82. Process Decision Points: MicroCC Completes this
Decision Sequence for each term of each Student
1) Enrollment 3) Number of
/re-enrollment courses
choice in each attempted
term
2) Full vs Part Time 4) Successful
enrollment in completion
each term of each course
attempted
27

83.  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.
28

84. Source: 7,310 ATE Students in Connecticut CCs
2000-2009
29

85.  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.
30

86.  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.
31

87.  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
32

88. 33

89. Source: 7,310 ATE Students in Connecticut CCs
2000-2009
34

90. Women Outpace Men in all Race Categories -
Percent of Students Completing their Programs
by Gender & by Race in Conn. N=7,310 ATE students
60
50
49
50 48
43
40
37
35
30 Men
Women
20
10
0
White Black Hispanic
35

91.  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.
For More information contact Ron Anderson
rea@umn.edu or 952-473-5910
36

92. 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
37

93.  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
38

94. 39

95.  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.
40

96. For more information contact:
Ron Anderson
rea@umn.edu
41