The First Two Years of College Math:
Building Student Success
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
The First Two Years of College Math:
Building Student Success
Evolution of Math Undergraduate
Education for the Physical Sciences
STEM Readiness, Modeling and
Computational Science
Peter Turner
SIAM Vice President for Education
Dean of Arts & Sciences,
Professor of Mathematics and Computer Science,
Clarkson University
pturner@clarkson.edu vpeducation@siam.org
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• Outdated curricula and delivery methods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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Building Student Success
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Background to STEM
Readiness Problem
Retention is a high priority
Near-unique institution facing common issues
Small scale makes us nimble
• 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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Building Student Success
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The elevator pitch!
“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.
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Building Student Success
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STEM Readiness
• Major issue even for highly selective, STEMintensive 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 twodimensional model of STEM readiness
• Advising tool for “placement”
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Building Student Success
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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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Building Student Success
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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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Building Student Success
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Modeling across the
Curriculum
NSF/EHR/DUE Awards 1206230 & 1352973,
Education and Human Resources Directorate
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Building Student Success
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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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The First Two Years of College Math:
Building Student Success
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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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The First Two Years of College Math:
Building Student Success
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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
* Credit to Jeff Humpherys for some of this content
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Building Student Success
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Teacher Ed
Educational Pipeline Flow
Primary &
Undergrad.
Graduate
Secondary
Curriculum
Curriculum
Curriculum
Science and Technology Industry
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
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Building Student Success
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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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Building Student Success
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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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Building Student Success
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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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Building Student Success
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Future of CS&E
Education
SIAM-EESI Workshop
Breckenridge, CO
August 2014
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Building Student Success
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CSE Future Workshop
• Graduate and Undergraduate Education
• Future research directions, too
• Potential updates to
• Petzold report on CSE Grad Education
SIAM Working Group on CSE Education (Linda Petzold, Chair) Graduate
Education in CSE, SIAM Review 43 (2001) 163-177
• Turner/ Petzold report on Undergrad CSE Education
SIAM 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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The First Two Years of College Math:
Building Student Success
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Computational Science
and Engineering
Computer
Science
Mathematics
CSE
Science &
Engineering
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The First Two Years of College Math:
Building Student Success
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".
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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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Building Student Success
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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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Building Student Success
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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
The First Two Years of College Math:
Building Student Success
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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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Building Student Success
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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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Building Student Success
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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 –
and to DRIVE CHANGE
Kirk Jordan, IBM
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Building Student Success
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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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Building Student Success
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