1. 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

2. 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

3. 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 INSTANCES, XSEDE13

4. Module Content Teacher Materials – Learning objectives – Model validations – CST goals, objectives – Background readings Student Reading (culled) Exercises “Programming” Implementations – Excel, Python, Vensim INSTANCES, XSEDE13

5. 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 INSTANCES, XSEDE13 Modules taught Spontaneous Decay & Bugs (exponential growth) Excel, VenSim, Python Student Readings Exercises

6. 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 INSTANCES, XSEDE13 Feedback from 20 PTs on their perceptions of the applications

7. 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 Module Collection

8. E.G.: Limits and Precision INSTANCES, XSEDE13 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

9. E.G.: Limits and Precision CST Computers = experimental lab Computers = finite Range: natural, compute numbers → Floating pt numbers ǂ exact Student exercises INSTANCES, XSEDE13 Precision in Excel VenSim

10. E.G.: Random Numbers INSTANCES, XSEDE13 CST Pseudo-random numbers Need look, check numbers Introduce chance into computing Stochastic natural processes

11. E.G.: 3-D Random Walks INSTANCES, XSEDE13 3D Walk.py Perfume diffusion Brownian motion

12. INSTANCES, XSEDE13 E.G.: Spontaneous Decay Simulation INSTANCES, XSEDE13 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

13. INSTANCES, XSEDE13 E.G.: Stone Throwing Integration INSTANCES, XSEDE13 CST New way to do math (stochastic) Calculus via experiment Rejection technique

14. 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 INSTANCES, XSEDE13

15. K-12 Standards INSTANCES, XSEDE13 “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.