materials for teacher ed classes rubin landau (pi), oregon state university physics raquell holmes,...
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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.