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Common themes in the Recently Adopted Undergraduate and Master's Curriculum Recommendations
A. John Bailer
Abstract #314570, Common themes in the Recently Adopted Undergraduate and Master's Curriculum Recommendations Session # 12 on Sunday, 8/9/2015 beginning at 2:00 PM
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Outline 1. Recap of the Master’s degree workgroup – process and
outcomes 2. Comparison of recommendations, key points, pedagogy,… 3. Wrap up
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1. Recap of the Report of the Master’s Degree Learning Outcomes Workgroup
workgroup was formed by Bob Rodriguez, 2012 ASA (American Statistical Association) President
Members of the workgroup: John Bailer, Roger Hoerl, David Madigan, Jill Montaquila, Tommy Wright.
Statement of task:
Develop guidelines, framed as learning outcomes, for master’s degree programs in statistics and biostatistics that are responsive to the needs of stakeholders who employ such graduates.
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Strategy:
Conducted two surveys i. recent Master’s graduates ii. employers of Master’s graduates
Timeline:
i. Constructed survey forms and submitted them for review by the ASA Survey Committee (Fall 2011)
ii. Compiled list of recent graduates from contacting ASA Caucus of Academic Reps and other schools not covered by the Caucus (Fall 2011)
iii. Sample of recent graduates selected and contacted for phone survey (Spring 2012)
iv. List of employers compiled from solicitation from ASA Board of Directors, Caucus of Academic Reps and other contacts (Summer 2012)
v. Employer survey emailed with reminder sent two weeks later (Fall 2012) vi. Results compiled, report produced and disseminated (late Fall 2012)
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Results – Recent graduates
1. Names of 366 graduates provided by 21 schools 2. Number of graduates/school ranged from 1 to 110 (M=12,
Q1=7, Q3=17) 3. Interviews were completed for 29 recent graduates from the
115+ contacted from 13 different schools 4. Responses to interview questions were grouped into
common topic categories
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Knowledge/Skills learned in school that helped you perform your first job? (double arrows: 10+ of 29; single arrow: 3 of 29)
Knowledge/skills learned
in grad school that helped
GET FIRST JOB
Program-
ming
Specific stat
method
Stat/ math
bkgd
Experience
Commun-
ication
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Knowledge/Skills that helped you perform your first job? (double
arrow: 10+ of 29)
v
Knowledge/skills helped
PERFORM 1st
JOB
Program-
ming
Specific stat
method
Stat/ math
bkgd
Experience
Commun-
ication
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Knowledge/skills graduates wished they had more in school (double
arrows: 12 (programming) / 7 (specif stat method) of 29; single thin arrow: 3- of 29)
WISHED HAD
MORE …
Program-
ming
Specific stat
method
Experience
Commun-
ication
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Swapping courses in grad programs (double arrows: 6-7 of 29)
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Results – Employers
1. Names and contacted information for 68 employers of Master’s graduates were generated.
2. Responses for 19 employers (28%) were generated from an email contact with a follow-up reminder two weeks later.
3. Note: two employers responded with a general email that did not explicitly address specific questions.
4. Employer organizations: university-based collaborative study
centers/academic medical research settings (5), federal government (3 ),
contract research organizations (2), survey organiz. (2), and
financial/banking (2). Remaining organizations (each 1) : clinical trials
consulting, pharmaceutical, public policy non-profit, consumer products,
clinical research organization, or manufacturing.
5. Responses to interview questions were grouped into common topic
categories (dark, double arrows => more than 10 respondents mentioned
this topic; single arrows => 3-8 respondents)
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Characteristics of the top two candidates (double arrows: 10+ [14
communication] of 29; single arrow: 8- [8=attitude] of 29)
TOP TWO
candidates
Program-
ming
Advanced
stat method
Stat/ math
bkgd
Attitude /
Personality
Commun-
ication
Thinking /
problem solving
Collaborative /
teamwork
Experience
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What made hires successful? (double arrows: 10+ [14 attitude/personality] of
29; single arrow: 6- [6 thinking/problem solving of 29)
Made Hires
Successful
Program-
ming
Stat/ math
bkgd
Attitude /
Personality
Commun-
ication
Thinking /
problem solving
Collaborative /
teamwork
Experience
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What knowledge/ skills were required and critical? (double arrows:
10+ [12 communication] of 29; single arrow: 5- [5 adv stat methods & communic.] of 29)
REQUIRED and
CRITICAL skills
Program-
ming
Stat/ math
bkgd
Attitude /
Personality
Commun-
ication
Thinking /
problem solving
Collaborative /
teamwork
Experience
Advanced
stat method
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Deficits in recent hires? (double arrow: 9 of 29; single arrow: 6- [6
communication] of 29)
Deficits in
recent hires
Program-
ming
Stat/ math
bkgd
Attitude /
Personality
Commun-
ication
Thinking /
problem solving
Collaborative /
teamwork
Advanced
stat method
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Master’s Degree Workgroup Recommendations 1: Graduates should have a solid foundation in statistical theory and methods. 2: Programming skills are critical and should be infused throughout the
graduate student experience. 3: Communication skills are critical and should be developed and practiced
throughout graduate programs. 4: Collaboration, teamwork, and leadership development should be part of
graduate education. 5: Students should encounter non-routine, real problems throughout their
graduate education. 6: Internships, co-ops or other significant immersive work experiences should
be integrated into graduate education. 7: Programs should be encouraged to periodically survey recent graduates
AND employers of their recent graduates. Evaluate success of their programs and to examine if other programmatic changes are warranted.
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Report: http://www.amstat.org/education/pdfs/PMSSS.pdf Report: http://magazine.amstat.org/wp-content/uploads/2013an/masterworkgroup.pdf Project INGenIOuS strategies for advancing the mathematics and statistics workforce http://www.ingeniousmathstat.org/
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2. Comparison of recommendations in Master’s workshop report and the curriculum guidelines for undergraduate programs in statistical science
Master’s recommendations Ugrad KEY POINTS Strong Foundation in stat theory and methods
Programming skills infused throughout the grad experience
Increased importance of data science
Communication skills critical –need to be developed & practiced
Ability to communicate
Collaboration, teamwork & leadership development
Encounter non-routine, real problems More diverse models and approaches; Real applications
Internships, co-ops or other significant immersive work experiences
Survey recent grads and employers
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Master’s recommendations Ugrad SKILLS NEEDED Strong Foundation in stat theory and methods
Statistical methods and theory; Mathematical Foundations
Programming skills infused throughout the grad experience
Data manipulation and computation
Communication skills critical –need to be developed & practiced
Statistical Practice
Collaboration, teamwork & leadership development
Encounter non-routine, real problems Discipline-Specific knowledge Internships, co-ops or other significant immersive work experiences
Survey recent grads and employers
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Master’s recommendations Ugrad CURRICULUM FOR STAT MAJOR Strong Foundation in stat theory and methods
Stat methods and theory (Stat Theory, EDA, Design, Models); Math Foundations
Programming skills infused throughout the grad experience
Data manipulation and computation (use of 1+ prof stat software, data manip – documented, reproducible, prog concepts, comp intensive mthds)
Communication skills critical –need to be developed & practiced
Stat practice (interact & commun with clients; eff tech writing, present, viz)
Collaboration, teamwork & leadership development
Stat practice (teamwork & collaboration)
Encounter non-routine, real problems Internships, co-ops or other significant immersive work experiences
Survey recent grads and employers
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Master’s recommendations Ugrad Pedagogical Considerations Strong Foundation in stat theory and methods
Encourage synthesis of theory, methods, computation and applic.
Programming skills infused throughout the grad experience
Exp with stat computing and data-related skills early and often
Communication skills critical –need to be developed & practiced
Freq opportunities to refine communicate skills tied to instr in tech stat skills
Collaboration, teamwork & leadership development
Provide opportunities to work in teams
Encounter non-routine, real problems Develop flexible problem solving skills; problems with substantive context
Internships, co-ops or other significant immersive work experiences
Survey recent grads and employers
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Summary
Topic
Master’s workgroup rec.
Ugrad curric guidelines
Strong Foundation in stat theory and methods
√ √
Programming skills infused throughout the grad experience
√ √
Communication skills √ √ Collaboration, teamwork & leadership development
√ √
Encounter non-routine, real problems
√ √
Internships, co-ops or other significant immersive work experiences
√ ± (could be viewed as natural extension of particular guidelines)***
Survey recent grads and employers
√ √ (encouraged in ‘Next Steps’)
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3. Wrap-up
While the process to generate recommendations differed
between the two workgroups (Master’s and ugrad curriculum
guidelines), the identified topics and categories were
remarkably similar
The surveys of recent graduates and employers gave some
relative weighting of categories …
Graduates
o noted that PROGRAMMING skills, stat/math bkgd and
specific methods helped get first jobs
o performance was impacted by these skills and
COMMUNICATION and EXPERIENCE.
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Employers
o PROGRAMMING (broadly defined) was associated with
being a top candidate, successful hire, deficit in some,
required, critical and desired skill by employers of recent
graduates.
o STAT/MATH BKGD and COMMUNICATION were the
associated with top candidates, successful hires and
required and critical skills.
Deficit in these skills = not hired
Successful hires also possessed skills in
COLLABORATION, TEAMWORK, LEADERSHIP
ATTITUDE / PERSONALITY
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Final thoughts:
Exciting to see this convergence in undergraduate and Master’s
degree recommendations
Challenge will be in implementation – For example, what does
‘infusing the computing in the curriculum’ mean? Are faculty
members prepared to do this?
How would doctoral degree requirements differ? { conjecture:
many categories in common but preparation for independent
research would change the weights }
Implications for training the next generation of statistics faculty
members?
Implication for retraining the current generation of statistics faculty
members?
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Thank you for your interest!
John Bailer
Department of Statistics Miami University Oxford, Ohio 45056 USA T: 513.529.7828 (STAT!) [email protected]