Engineering Education & Modeling
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Engineering Education & Modeling

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Lecture 2 of the course Understanding Enginees by David E. Goldberg reviews how engineers are educated and the use of models in engineering. The course is taught in Labor and Employment Relations at ...

Lecture 2 of the course Understanding Enginees by David E. Goldberg reviews how engineers are educated and the use of models in engineering. The course is taught in Labor and Employment Relations at the University of Illinois to masters students in human resources.

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Engineering Education & Modeling Engineering Education & Modeling Presentation Transcript

  • Engineering, Education & ModelingLER 590 - UE Week 2: From Math & Science to Modeling & Design
    David E. GoldbergIESE, IFoundry, and School of Labor and Employment RelationsUniversity of Illinois at Urbana-ChampaignUrbana, Illinois 61801 USAdeg@illinois.edu
  • Engineering: Education vs. Practice
    Math-science death march in first two years of engineering education is misleading.
    Implies: Meaningful models come from math & science alone.
    Young engineers confused on first jobs.
    2 problems:
    Don’t use appropriate models.
    Don’t think they’re doing engineering.
    Need better understanding of (1) origins of engineering education, (2) types & breadth of engineering modeling.
  • Roadmap
    What is engineering?
    Engineering education, early days.
    Engineering education today.
    What is a model?
    Legacy of Newton: equations versus words & images.
    Matching models to problems.
    What are models used for.
    Tech visionaries as broad spectrum modelers.
    Toulmin’s model of arguments as unifying approach.
    Return to flour physics.
    Simulated engineering experience 2 – SEE-2.
    View slide
  • What is Engineering?
    You’re sitting in class at University of Illinois at Urbana-Champaign
    Has one of the best engineering schools on the planet.
    What is engineering?
    How would you define it?
    © David E. Goldberg 2009
    Mechanical Building & Drill Hall 1871
    View slide
  • A Definition?
    Here: Engineering is the social practice of conceiving, designing, implementing, producing, & sustaining complex artifacts, processes, or systems appropriate to some recognized need.
    Artifacts primary object.
    Science & math are among tools used for artifact conception & support.
    Social practice Engineered by and for people.
    Social side as important as the physics.
    Some engineered objects are physical, but all engineered objects are social.
    © David E. Goldberg 2009
  • Engineering Education: Origins
    France: Corps du Genie (1676)  school, 1749; Corps des Ponts et Chausees (1747) Ecole de Ponts et Chausees (1775), 1st formal engineering school in world. 1794, Napoleon created Ecole des Travaux Publics EcolePolytechnique: Heavy math.
    Britain: Royal Military Academy (Woolrich, 1741), but most works through apprenticeship, practical, enterprising men.
    US: hybrid, adopted French texts & curriculum, but English tradition held sway as well.
    1778 Engineering department in US forces, 1779 as corps. Military school at West Point 1794  USMA in 1802
    Mechanics institutes: 1850 around 6000 including Franklin Institute.
    1824 Rensselaer School  1835 Rensselaer Institute
    University of Virginia, 1833 courses in Civil Engineering
    University of Alabama, 1837 civil engineering in connection with railroads.
  • Engineering Education Today
    Use Illinois curricula for IIGs.
    Get into your IIGs.
    Review curricula from your discipline.
    Analysis: Hours, categories, distribution, advanced technical content.
    Comparison: Other degree types (LAS, ?).
    Thoughts about preparation for world of work.
  • What is a Model?
    A model is a system that represents one or more facets of some other system.
    Typical model  facet combinations:
    Drawing or solid model  geometry.
    Prototype  geometry & operation.
    Graph  variation of variable with independent variable(s) (time, space, etc.).
    Equilibrium equation  select state variables at steady state.
    Dynamic equation  variation of select state variables with time.
    Computer simulation  similar to equations.
  • Models & Engineering Knowledge
    Models capture what engineers know about what exists or could exist.
    Study of knowledge: Epistemology.
    Study of existence: Ontology.
    What engineers know and how they know it?
    Walter Vincenti’sbook takes examples from aeronautical history.
    Discusses distinctions between engineering knowledge and scientific-mathematical knowledge.
  • Newton & Engineering Models
    Invented calculus (so did Leibniz).
    In 1687 published Philosophiae Naturalis Principia Mathematica.
    Changed world.
    Remarkable agreement between equations & measurements.
    Many engineering models use his equations (F=ma) directly.
    Represents an ideal of scientific knowledge that others attempt to emulate.
    Highest status accorded to those who use these kinds of models.
    Does status = efficacy?
    Sir Isaac Newton (1643-1727)
  • Words, Language and Engineering Models
    Newton-style models dominate engineering equations.
    Engineers often use natural language in modeling.
    Many first models are verbal.
    Types of verbal models:
    Single word or noun phrase.
    Description of an object/process.
    Feature list.
    Dimension list.
    Set of engineering specifications, standards, or claims.
  • Images and Engineering Modeling
    History of drawings and visual representations of engineered objects is long.
    Downgrading of engineering visualization and drawing since Cold War.
    Ferguson’s book argues this was/is educational mistake.
  • Diagrams as Models
    Diagrams can be models.
    Drawings can be models.
    The Back of the Napkin connects visual thinking and verbal thinking in important way.
  • How to Match Models to Engin Problems
    What characterizes an appropriate model in engineering?
    What do you think?
    Take out a piece of paper and write down 3 attributes that suggest you have a good model.
    2 minutes.
  • An Economy of Models
    Engineers think in terms of models.
    Have many models with different precision-accuracy and different costs.
    Can we distinguish appropriate engineering model usage from that of scientist?
    The economics of modeling.
    Engineers use models in economic context  model usage must support objectives within available resources.
  • Fundamental Modeling Tradeoff
    Engineer/Inventor
    ε, Error
    Scientist/Mathematician
    C, Cost of Modeling
    Error versus cost of modeling
  • Spectrum of Models
  • What Are Models Good For?
    Many uses for models:
    Description: describe the ways things are (were).
    Prediction: describe the ways things will be.
    Prescription: describe the way things should be.
    Key variables: time and change.
    Usually assumes have extant object to model.
  • Research on Tech Visionaries as Clue
    Helpful to look at extreme exemplars of success.
    Price, Vojak, & Griffin have done work on tech visionaries (TVs).
    TV creates bottom line revenue from new products & services.
    TVs are consummate broad-spectrum modelers.
    Use qualitative-quantitative models as necessary to bring monster products/services to market.
    Ray Price
  • Key Distinction: Imagined vs. Existing
    Modeling of imagined or desired objects versus extant objects (recall category creatory vs. enhancer).
    To model imagined or desired objects, what can we draw upon?
    Existing objects that fail in some regard.
    Similar or related objects.
    Analogically related objects.
    Creatively concocted objects.
    Problem of the tabula rasa: How to model that which does not exist.
    In category creation, more modeling will be to the left (qualitative versus quantitative).
    Category enhancement: improvements require more precision. More modeling to the right (quant over qual).
  • How can we model the burnt-flour-as-mold problem?
    Newton’s laws?
    Need framework to tie different models together.
    Inside a Tortilla Factory
    21
  • Help from Argumentation Theory
    1958 book by philosopher Stephen Toulmin formed basis of argumentation theory.
    How do people really make arguments?
    How do people give reasons for what they think or do?
    Form of reasoning ties together formal and informal engineering reasoning.
  • Formal Reasoning: Logic
    Modus ponens (modus ponendo ponens: mode that affirms by affirming):
    if pthen q
    pis true
    thereforeqis true
    Method of mathematical logic & formal reasoning.
    Note: Once premises and rules in place, formal logic derives conclusions mechanistically.
    Aristotle (384-322 BCE)
  • Toulmin: Elements of a Human Argument
    Like modus ponens:
    Claim. A single statement advanced for the adherence of others.
    Grounds. A statement about persons, conditions, events, or things that says support is available to provide a reason for a claim.
    Warrant. A general statement that justifies using the grounds as a basis for the claim
    Backing. Any support (specific instance, statistics, testimony, values, or credibility) that provides more specific data for the grounds or warrant.
    Qualifier. A statement that indicates the force of the argument (words such as certainly, possibly, probably, usually, or somewhat).
    Warrants can be generalizations, cause, sign, analogy, authority.
    Backing can be anecdote, stats, testimony, credibility, and values.
    Rieke, R. D & Sillars, M. O. (1997). Argumentation and critical decision making. New York: Longman.
  • Back to the Tortillas: Burnt Flour Model
    Grounds. Dusting flour is spread onto the moving dough on a continuous tortilla line.
    Claim. Burnt black flour deposits is mistaken for mold, resulting in quality complaints
    Warrant. Excess flour can become airborne and burn in the oven, deposit on tortilla.
    Qualifier. Sometimes
    Backing. Client story & increased flour results in increased spot problem.
  • Tradeoff: Improve Backing or Solve Problem
    In resource limited environment, often face decision:
    Should you improve warrant and backing?
    Or should you work on solving the problem?
    Difficult choice: If you assume correctness of warrant/backing & you are wrong, will it prevent you from solving problem.
    In tortilla problem students took explanation as true because it didn’t affect investigation.
  • SEE-2: Lighting an LED
    Illinois & the Light-Emitting Diode (LED).
    Nick Holonyak, Jr. invented LED in 1962 at GE.
    BS, MS, PhD (1954) from UIUC.
    John Bardeen’s first PhD student at Illinois
    Also invented semiconductor laser.
    Holder of 41 patents &
    Nick Holonyak, Jr, (b. 1954)
  • Summary
    Engineering students are convinced that math and physics are the main (only?) tools of engineering.
    Real engineers use a spectrum of models from qualitative to quantitative.
    Economy of modeling separates engineering from scientific practice.
    Toulmin’s model of arguments introduced & example from flour physics given.
  • Bottom Line
    Modeling is critical engineering activity, but don’t let emphasis on math-science mislead.
    Great engineers and tech visionaries are broad-spectrum modelers.
    Use simplest models that will advance design objectives (economy of modeling).
    Unify models by using Toulmin’s model of arguments & use explicitly to tradeoff model improvement vs. design.
    Will consider implications for HR & corporate training later.