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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!) baileraj@MiamiOH.edu