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 DUE

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 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 – 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 – 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

SED 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

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

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

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

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

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

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

INSTANCES, XSEDE13 E.G.: Stone Throwing Integration INSTANCES, XSEDE13 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 INSTANCES, XSEDE13

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.