Peter Turner Clarkson University John Bailer Miami University Paul Zorn St Olaf College The First Two Years of College Math Building Student Success STEM Readiness Modeling Computational Science ID: 622501
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Evolution of Math Undergraduate Education for the Physical Sciences
Peter Turner, Clarkson UniversityJohn Bailer, Miami UniversityPaul Zorn, St. Olaf College
The First Two Years of College Math: Building Student Success
STEM Readiness, Modeling, Computational Science
Statistics and statistical modeling
INGenIOuS
and workforce issuesSlide2
Evolution of Math Undergraduate Education for the Physical Sciences
STEM Readiness, Modeling and Computational Science
The First Two Years of College Math: Building Student Success
Peter Turner
SIAM Vice President for Education
Dean of Arts & Sciences,
Professor of Mathematics and Computer Science,
Clarkson University
pturner@clarkson.edu
vpeducation@siam.orgSlide3
Key issues: Some of them
PCAST Engage to ExcelThe Math GapPreparation & Readiness for STEM majors
CU STEM admissions data Outdated curricula and delivery methodsMath 2025“Real-life” relevant contentStudent “demands” for relevant education
BUT with care over “training vs. education”
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
3Slide4
Background to STEM Readiness Problem
Budget is dominated by tuitionClose to 90% STEM majorsLong-established demanding curriculum had little flexibilityNo remedial/catch up courses available in regular program
Calculus, Physics and Chemistry (I & II) all in First YearStarted to change in early 2000’sPredictor-Corrector-Refinement model
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
4
Retention is a high priority
Near-unique institution facing common issues
Small scale makes us nimbleSlide5
The elevator pitch!
CBMS Forum October 2014
The First Two Years of College Math: Building Student Success
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“Dismissed” means for academic reasons only
What we’re doing is working!
Note that “treatments” have been focused primarily on ENG/STEM majors so far.Slide6
STEM Readiness
Major issue even for highly selective, STEM-intensive collegesClarkson has close to 65% of incoming STEM majors under-prepared in Math Based on diagnostic test of pre-calc skillsExpectation of starting in
Calc I (or higher)Used in conjunction with a Physics concept survey (FCI) to give a highly predictive two-dimensional model of STEM readinessAdvising tool for “placement”
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
6Slide7
STEM Readiness
Just a part of a comprehensive retention programIncludes Spatial VisualizationWriting assessmentCounseling and non-academic advising, too92% first-year retention in Fall 2013 cohortAdding more hands-on experiences in first year
Teach the students you haveAdd relevance and “real-life” projectsConnect the dots
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
7Slide8
The Curriculum: What is being done?
Multiple initiatives in the Math Sciences communityModeling across the CurriculumTPSE-Math
MAA-led Common Vision for Undergraduate Math in 2025Computational Science & Engineering Future Workshop GAISE (Statistics assessment)SIAM & COMAP are collaborating on a similar initiative in Math Modeling, GAIMME
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
8Slide9
Modeling across the Curriculum
CBMS Forum October 2014The First Two Years of College Math: Building Student Success
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NSF/EHR/DUE Awards
1206230 &
1352973
,
Education and
Human
Resources Directorate Slide10
MaC I Recommendations
Undergraduate programsDevelop modeling-based undergraduate curriculaAdvocate an infusion model, “Trojan mice”
Addresses the PCAST Math GapOpportunities for coordinated approach to math and science teachingStudio Calculus project
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
10Slide11
MaC I Recommendations
Undergraduate programsDevelop a repository of materials for math modeling instruction and understanding
No organized progress yetSimilar theme emerged at TPSE MathDistinction between Models and ModelingNot just math majors
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
11Slide12
Some MaC II undergrad recommendations*
Proposal for NRC Study/ReportResponse to Joan Ferrini-Mundy’s Challenge to think about effective ways to educate students at the crossroads of:Mathematical modeling
Data scienceInformation scienceComputational scienceComputational thinking
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
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* Credit to Jeff
Humpherys
for some of this contentSlide13
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
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SIAM Working Group On CSE Undergraduate Education (Turner and Petzold, co-chairs)
Undergraduate Computational Science and Engineering Education
, SIAM REVIEW Vol. 53, No. 3, pp. 561–574
http://epubs.siam.org/doi/pdf/10.1137/07070406X
Slide14
Modeling and the Pipeline:
Attracting and retaining STEM students
How to achieve the 34%
increase in
Engage to
Excel
.
Recruitment and
retention
Appeal to diverse population
Multiple entryways?
A non-calculus track for freshman modeling?Use of computation/ discrete calculusData-based models as well as
“physics-based” models
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
14Slide15
Modeling and the Pipeline:
Attracting and retaining STEM students
Multiple math science major programsNot uniform across institutions
Increased statistics and data science
Modeling and solution of models
Computational, analytic, simulation-based
What if scenarios
Linkage/ coordination with applications
domains
Require a minor?
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
15Slide16
What are “new” key areas for undergrad math?
A modern math sciences undergraduate education should include at least some introduction toAlgorithms and Analysis (Data Structures, Approximation Theory, Numerical Analysis, Computational Science)
Distributed Computing and Big Data (MPI, Hadoop, noSQL)Data Analytics (Regression, Estimation, SQL, R/Python)
Modeling with Probability and Stochastic Processes
Bayesian Statistics and Machine Learning
Dynamical Systems
(ODE, PDE, SDE)
Optimization
and Control
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
16Slide17
Future of CS&E Education
CBMS Forum October 2014The First Two Years of College Math: Building Student Success
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SIAM-EESI Workshop
Breckenridge, CO
August 2014Slide18
CSE Future Workshop
Graduate and Undergraduate EducationFuture research directions, tooPotential updates toPetzold report on CSE Grad EducationSIAM Working Group on CSE Education (Linda Petzold, Chair)
Graduate Education in CSE, SIAM Review 43 (2001) 163-177Turner/ Petzold report on Undergrad CSE EducationSIAM Working Group On CSE Undergraduate Education (Turner and Petzold, co-chairs)
Undergraduate Computational Science and Engineering Education
, SIAM REVIEW
53 (2011)
561–574
http://epubs.siam.org/doi/pdf/10.1137/07070406X
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
18Slide19
Computational Science and Engineering
CBMS Forum October 2014
The First Two Years of College Math: Building Student Success
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CSE is larger than the pure intersection of the three component pieces, but is nonetheless included in their union.
That is to say CSE provides, and strengthens, the bridges connecting those components but should not become a separate "island". Slide20
Why is CSE education relevant here?
The basic models – and philosophy – of CSE programs apply equally well to programs in the Math Sciences as a whole, especially in transitional yearsUsing relevant learning experiences
Making connections to other STEM fields, while Introducing sound mathematical concepts and reasoning Focus on integration of knowledge to develop problem-solving methodologies &
tools
Needs
input/collaboration from application
domains
Advocating for internships and career preparation
Simultaneous development of vital “soft skills”
Building bridges, not silos
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
20Slide21
Can this work in the transition years?
Emphatic “Yes”I was personally involved for some 15 years at USNA with the Computer Calculus sequence
Satisfied both Calc and CS requirementsCoordinated throughout
Deeper
understanding of many fundamental concepts
Included rigorous proofs and applications of uniform continuity and development of the Riemann integral at freshman level
University of Oslo (Knut
M
ø
rken
)
Computational projects in early courses for both STEM and non-STEM
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
21Slide22
Common Curriculum Content
Modeling and Simulation Data and science-based
Programming and algorithmsApplied math
Numerical methods
Parallel programming
Scientific
visualization
Analysis of results
Does my answer make sense?
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
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Application domain content
Team-based projects
Technical analysis and presentation
Research or “Professional” ExperienceSlide23
Motivational Factors for Developing CSE Programs
Future jobs of technical nature require new skills directly related to computational, including data and statistical, scienceComputer science graduates do not have the modeling, mathematics and science background needed for future technical employment
STEM fields are becoming more computational; science and engineering are now commonly done in silicoBoeing aircraft design process for example
Provides relevance to mathematics programs
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
23Slide24
Undergraduate Math
Sci Education Must AddressProfessional Experience or Internships
ProjectsInterdisciplinary, Team-based, including team teachingExtended projects develop perseverance for workplaceBreadth vs. Depth
Communication
Presentations at meetings
Educational outreach activities
Career awareness is critical to recruitment
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
24Slide25
An Industry perspective: What Industry Needs
Strong foundation in a disciplineNeed computational skillsNot just MATLAB
Understand Error, Stability, PerformanceNeed second discipline “expertise”Speak another “language”Provide added breadth
Transition to other problem areas
Willingness to Change –
and
to DRIVE
CHANGE
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
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Kirk Jordan, IBMSlide26
Conclusions and Recommendations
Many different models of undergraduate math sciences programs can workMany curricular items in commonMany different
objectives Other STEM disciplines at both undergrad and grad student levelsEducation, Graduate Schools, Labs, IndustryInterdisciplinary collaboration an integral part of the curriculum and thesis research
CBMS Forum October 2014
The First Two Years of College Math:
Building Student Success
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