evolution of math undergraduate education for the physical sciences
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Evolution of Math Undergraduate Education for the Physical Sciences. Peter Turner, Clarkson University John Bailer, Miami University Paul Zorn, St. Olaf College. STEM Readiness, Modeling, Computational Science Statistics and statistical modeling INGenIOuS and workforce issues. - PowerPoint PPT PresentationTRANSCRIPT
Evolution of Math Undergraduate Education for the Physical Sciences
Peter Turner, Clarkson UniversityJohn Bailer, Miami University
Paul Zorn, St. Olaf College
The First Two Years of College Math: Building Student Success
STEM Readiness, Modeling, Computational ScienceStatistics and statistical modelingINGenIOuS and workforce issues
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 [email protected] [email protected]
Key issues: Some of them
• PCAST Engage to Excel• The Math Gap
• Preparation & Readiness for STEM majors• CU STEM admissions data
• Outdated curricula and delivery methods• Math 2025• “Real-life” relevant content
• Student “demands” for relevant education• BUT with care over “training vs. education”
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Background to STEM Readiness Problem
• Budget is dominated by tuition• Close to 90% STEM majors• Long-established demanding curriculum had
little flexibility• No remedial/catch up courses available in regular
program• Calculus, Physics and Chemistry (I & II) all in First Year
• Started to change in early 2000’s• Predictor-Corrector-Refinement model
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Retention is a high priorityNear-unique institution facing common issues
Small scale makes us nimble
The elevator pitch!
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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.
STEM Readiness• Major issue even for highly selective, STEM-
intensive colleges• Clarkson has close to 65% of incoming STEM
majors under-prepared in Math • Based on diagnostic test of pre-calc skills• Expectation 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 readiness
• Advising tool for “placement”
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STEM Readiness
• Just a part of a comprehensive retention program• Includes Spatial Visualization• Writing assessment• Counseling and non-academic advising, too
• 92% first-year retention in Fall 2013 cohort• Adding more hands-on experiences in first year • Teach the students you have• Add relevance and “real-life” projects• Connect the dots
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The Curriculum: What is being done?
• Multiple initiatives in the Math Sciences community
• Modeling across the Curriculum• TPSE-Math• MAA-led Common Vision for Undergraduate Math
in 2025• Computational Science & Engineering Future
Workshop • GAISE (Statistics assessment)
• SIAM & COMAP are collaborating on a similar initiative in Math Modeling, GAIMME
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Modeling across the Curriculum
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The First Two Years of College Math: Building Student Success
NSF/EHR/DUE Awards 1206230 & 1352973, Education and Human Resources Directorate
MaC I Recommendations
Undergraduate programs•Develop modeling-based undergraduate curricula
• Advocate an infusion model, “Trojan mice”• Addresses the PCAST Math Gap• Opportunities for coordinated approach to
math and science teaching• Studio Calculus project
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MaC I Recommendations
Undergraduate programs• Develop a repository of materials for
math modeling instruction and understanding
• No organized progress yet• Similar theme emerged at TPSE Math
• Distinction between Models and Modeling
• Not just math majors
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Some MaC II undergrad recommendations* • Proposal for NRC Study/Report
Response to Joan Ferrini-Mundy’s Challenge to think about effective ways to educate students at the crossroads of:
• Mathematical modeling• Data science• Information science• Computational science• Computational thinking
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* Credit to Jeff Humpherys for some of this content
Science and Technology Industry
Primary &SecondaryCurriculum
Undergrad.Curriculum
GraduateCurriculum
Educational Pipeline Flow
Teacher Ed
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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
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 calculus• Data-based models as well as
“physics-based” models
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Modeling and the Pipeline:Attracting and retaining STEM students
• Multiple math science major programs• Not 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?
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What are “new” key areas for undergrad math?
A modern math sciences undergraduate education should include at least some introduction to• Algorithms 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
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Future of CS&E Education
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SIAM-EESI WorkshopBreckenridge, CO
August 2014
CSE Future Workshop
• Graduate and Undergraduate Education• Future research directions, too• Potential updates to
• Petzold report on CSE Grad EducationSIAM Working Group on CSE Education (Linda Petzold, Chair) Graduate Education in CSE, SIAM Review 43 (2001) 163-177
• Turner/ 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
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Computational Science and Engineering
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Mathematics Computer Science
Science & Engineering
CSE
Mathematics Computer Science
Science & Engineering
CSE
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".
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 years
• Using 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
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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 requirements• Coordinated 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 Mørken)• Computational projects in early courses for both STEM and
non-STEM
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Common Curriculum Content
• Modeling and Simulation
• Data and science-based• Programming and
algorithms• Applied math• Numerical methods
• Parallel programming• Scientific visualization
• Analysis of results• Does my answer make
sense?
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• Application domain content
• Team-based projects
• Technical analysis and presentation
• Research or “Professional” Experience
Motivational Factors for Developing CSE Programs
• Future jobs of technical nature require new skills directly related to computational, including data and statistical, science
• Computer 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 silico
• Boeing aircraft design process for example
• Provides relevance to mathematics programs
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Undergraduate Math Sci Education Must Address
• Professional Experience or Internships• Projects
• Interdisciplinary, Team-based, • including team teaching
• Extended projects develop perseverance for workplace
• Breadth vs. Depth• Communication
• Presentations at meetings• Educational outreach activities
• Career awareness is critical to recruitment
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An Industry perspective: What Industry Needs
• Strong foundation in a discipline• Need computational skills
• Not just MATLAB• Understand Error, Stability, Performance
• Need second discipline “expertise”• Speak another “language”• Provide added breadth• Transition to other problem areas• Willingness to Change –
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Kirk Jordan, IBM
Conclusions and Recommendations
• Many different models of undergraduate math sciences programs can work
• Many curricular items in common• Many different objectives
• Other STEM disciplines at both undergrad and grad student levels
• Education, Graduate Schools, Labs, Industry• Interdisciplinary collaboration an integral part
of the curriculum and thesis research
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