Materials for Teacher Ed Classes Rubin Landau (PI), Oregon State University Physics Raquell Holmes, improvscienceNamHwa Kang, OSU Science & Math EducationGreg Mulder, Linn-Benton Community CollegeSofya Borinskaya, UConn Health Center

National Science Foundation TUES Award 1043298-DUEhttp://science.oregonstate.edu/INSTANCES

Motivations • Computational Science view:

CS + math + science• Computation is essential in science • Simulation part of scientific process

• To change K-16, change Teacher Education • A single CS class not enough• Need ability to look inside application black box• Improved pedagogy via problem-solving context

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Motivations For Pre- & In-Service Teachers

•Science + scientific process•Include simulation, data & math •Complexity via simplicity•Numerical & analytic solutions• Data via computing Aim: teach computing use as part of science Disciplines: physics, biology

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Module Content

• Teacher Materials– Learning objectives– Model validations– CST goals, objectives– Background readings

• Student Reading (culled)• Exercises• “Programming”• Implementations

– Excel, Python, Vensim

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Classroom Context

Schools of Education•Science and Math Ed 412-413

– Pre-service teachers– Technology-Inquiry in

Math and Science– 1 week instruction– Post survey

•Computational Physics

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Modules taught•Spontaneous Decay & Bugs (exponential growth)•Excel, VenSim, Python•Student Readings•Exercises

SED 412-413

• 20 PTs, 14 respondents– 1 previous CS course– Most excel– No python or vensim

• Additional comments– Length of time to learn

application– Extensive background

material provided

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Feedback from 20 PTs on their perceptions of the applications

1. Computer Precision2. Spontaneous Decay3. Biological Growth4. Bug Population

Dynamics5. Random Numbers6. Random Walk7. Projectiles + Drag8. Trial & Error Search

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Module Collection

E.G.: Limits and Precision

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Computational Science Thinking

• Computers = experimental lab

• Computers = finite

• Range: natural, compute numbers

• → Floating pt numbers ǂ exact

• Student exercises

Limits.py

Limits in Excel

Limits in Python

E.G.: Limits and Precision

CST• Computers = experimental lab

• Computers = finite

• Range: natural, compute numbers

• → Floating pt numbers ǂ exact

• Student exercises

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Precision in Excel

VenSim

E.G.: Random Numbers

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CST

• Pseudo-random numbers

• Need look, check numbers

• Introduce chance into computing

• Stochastic natural processes

E.G.: 3-D Random Walks

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3D Walk.pyPerfume diffusionBrownian motion

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E.G.: Spontaneous Decay Simulation

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CST

• Sounds like Geiger (real world)?

• How know what’s real?

• Real meaning of simulation

• Meaning of exponential decay

DecaySound.py

Algorithm: if random < , decay

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E.G.: Stone Throwing Integration

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CST

• New way to do math (stochastic)

• Calculus via experiment

• Rejection technique

Conclusions

• Group challenge: level of math, of science• Early Assessment

– scientific process helps– balance: background vs exercises– disparity computing backgrounds– need literacy + programming tool

• Truer Effectiveness: need full course• Help! - Need science educator replacement,• Biology examples

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K-12 Standards

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“Understand numbers, ways of representing numbers, relationships among numbers, and number systems; Understand patterns, relations, and functions; Use mathematical models to represent and understand quantitative relationships; Use visualization, spatial reasoning, and geometric modeling to solve problems”

~Principles and Standards for School Mathematics, National Council of Teachers of Mathematics, 2000.