Materials for Teacher Ed Classes Rubin Landau PI

Materials for Teacher Ed Classes Rubin Landau (PI), Oregon State University Physics Raquell Holmes, improvscience Nam. Hwa 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, XSEDE 13

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, XSEDE 13

Module Content • Teacher Materials Learning objectives Model validations CST goals, objectives Background readings • Student Reading (culled) – – • Exercises • “Programming” • Implementations – Excel, Python, Vensim INSTANCES, XSEDE 13

Classroom Context Schools of Education • Science and Math Ed 412 -413 Modules taught • Spontaneous Decay & Bugs (exponential growth) • Excel, Ven. Sim, Python • Student Readings • Exercises – Pre-service teachers – Technology-Inquiry in Math and Science – 1 week instruction – Post survey • Computational Physics INSTANCES, XSEDE 13

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, XSEDE 13 Excel Ven. Sim Vpython

Module Collection 1. 2. 3. 4. 5. 6. 7. 8. Computer Precision Spontaneous Decay Biological Growth Bug Population Dynamics Random Numbers Random Walk Projectiles + Drag Trial & Error Search INSTANCES, XSEDE 13

E. G. : Limits and Precision Limits in Excel Computational Science Thinking • Computers = experimental lab • Computers = finite Limits in Python • Range: natural, compute numbers • → Floating pt numbers ǂ exact • Student exercises INSTANCES, XSEDE 13 Limits. py

E. G. : Limits and Precision in Excel CST • Computers = experimental lab Ven. Sim • Computers = finite • Range: natural, compute numbers • → Floating pt numbers ǂ exact • Student exercises INSTANCES, XSEDE 13

E. G. : Random Numbers CST • Introduce chance into computing • Pseudo-random numbers • Stochastic natural processes • Need look, check numbers INSTANCES, XSEDE 13

E. G. : 3 -D Random Walks Perfume diffusion Brownian motion 3 D Walk. py INSTANCES, XSEDE 13

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, XSEDE 13 Decay. Sound. py

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

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, XSEDE 13

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, XSEDE 13