Transcript Instances: Incorporating Computational Scientific Thinking

Materials for Teacher Ed Classes
Rubin Landau (PI), Oregon State University Physics
Raquell Holmes, improvscience
NamHwa Kang, OSU Science & Math Education
Greg Mulder, Linn-Benton Community College
Sofya Borinskaya, UConn Health Center
National Science Foundation TUES Award 1043298-DUE
http://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
INSTANCES, XSEDE13
Motivations
For Pre- & In-Service Teachers
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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
INSTANCES, XSEDE13
Module Content
• Teacher Materials
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Learning objectives
Model validations
CST goals, objectives
Background readings
• Student Reading (culled)
• Exercises
• “Programming”
• Implementations
– Excel, Python, Vensim
INSTANCES, XSEDE13
Classroom Context
Schools of Education
• Science and Math Ed
412-413
Modules taught
• Spontaneous Decay &
Bugs (exponential
growth)
• Excel, VenSim, Python
• Student Readings
• Exercises
– Pre-service teachers
– Technology-Inquiry in
Math and Science
– 1 week instruction
– Post survey
• Computational Physics
INSTANCES, XSEDE13
SED 412-413
• 20 PTs, 14 respondents
– 1 previous CS course
– Most excel
– No python or vensim
Feedback from 20 PTs on their
perceptions of the applications
14
12
10
Useful
8
• Additional comments
– Length of time to learn
application
– Extensive background
material provided
Willingness
Future
6
4
2
0
INSTANCES, XSEDE13
Excel
VenSim
Vpython
Module Collection
1. Computer Precision
2. Spontaneous Decay
3. Biological Growth
4. Bug Population
Dynamics
5. Random Numbers
6. Random Walk
7. Projectiles + Drag
8. Trial & Error Search
INSTANCES, XSEDE13
E.G.: Limits and Precision
Limits in Excel
Computational Science Thinking
• Computers = experimental lab
• Computers = finite
• Range: natural, compute numbers
Limits in Python
• → Floating pt numbers ǂ exact
• Student exercises
INSTANCES, XSEDE13
Limits.py
E.G.: Limits and Precision
Precision in Excel
CST
• Computers = experimental lab
• Computers = finite
VenSim
• Range: natural, compute numbers
• → Floating pt numbers ǂ exact
• Student exercises
INSTANCES, XSEDE13
E.G.: Random Numbers
CST
• Introduce chance into computing
• Pseudo-random numbers
• Stochastic natural processes
• Need look, check numbers
INSTANCES, XSEDE13
E.G.: 3-D Random Walks
Perfume diffusion
Brownian motion
3D Walk.py
INSTANCES, XSEDE13
E.G.: Spontaneous Decay Simulation
Algorithm: if random < , decay
CST
• Sounds like Geiger (real world)?
• How know what’s real?
• Real meaning of simulation
• Meaning of exponential decay
INSTANCES, XSEDE13
DecaySound.py
E.G.: Stone Throwing Integration
CST
• New way to do math (stochastic)
• Calculus via experiment
• Rejection technique
INSTANCES, XSEDE13
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,
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Biology examples
INSTANCES, XSEDE13
K-12 Standards
“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.
INSTANCES, XSEDE13