1. Texas Tech Oct 09 Rubin H Landau 1st = Computational subatomic few-body systems (1966-2003) 2nd = Research developments (1988-)  broaden, education Blended Multimodal Access to CP Curricula NSF (CCLI, CI-Team/EPIC), OSU, MSR © Rubin Landau, OSU Using Computation to Make Physics Education More Relevant & Accessible* Jo Nesbo Flaggermusmannen (The Bat Man)The Bat Man Kakerlakkene (Cockroaches)Cockroaches Rødstrupe (The Redbreast)The Redbreast Sorgenfri (Nemesis)Nemesis Marekors (The Devil's Star)The Devil's StarFrelseren (The Redeemer)The Redeemer Snømannen (The Snowman)The SnowmanPanserhjerte‘ (The Leopard)The Leopard

2. Texas Tech Oct 09 Contributing Group  Manuel J Paez, University of Medellin, Colombia, SA, CoAuthor  C. E. Yaguna, J. Zuluaga, Oscar A. Restrepo, Guillermo Avendano-Franco  Cristian Bordeianu, University of Bucharest, Romania, CoAuthor  Paul Fink, Robyn Wangberg, CoAuthors  Justin Elser, Chris Sullivan (system support)  Sally Haerer, Saturo S. Kano (consultants, producers)  Melanie Johnson (Unix Tutorials)  Hans Kowallik (Computational Physics text, sounds, codes, LAPACK, PVM)  Matthew Ervin Des Voigne (tutorials)  Bertrand Laubsch (Java sound, decay simulation)  Jon J Maestri (vizualizations, animations, quantum wave packets)  Guenter Schneider, Al Stetz, David McIntyre (First Course)  Juan Vanegas (OpenDX)  Connelly Barnes (OOP, PtPlot)  Phil Carter, Donna Hertel (MPI)  Zlatko Dimcovic (Wavelets, Java I/O)  Joel Wetzel (figures)  Pat Cannan, Don Corliss, Corvallis High School (N-D Newton Raphson)  Brian Schlatter  Daniel Moore, (REU, Summer 98; Whitman College, WA)  Justin Murray, (REU, Summer 98; Weber State University, Ogden, Utah  Brandon Smith, (REU, Summer 97; Chico State/SDSC, CA)  Paul D. Hillard, III (REU, Summer 96; Southern Univ, LA)  Kevin Wolver, (REU, Summer 96; St Ambrose, IA) And all the suffering students! © Rubin Landau, OSU

3. Texas Tech Oct 09 PlanPlan 1. What is CP Education? 2. Need for CP Education? 3. Elements of CP Education 4. How (Best) to Provide it? 5. NEW: Debut CP eTextBook 6. Take Home Lessons © Rubin Landau, OSU D of SC

4. Texas Tech Oct 09 © Rubin Landau, CPUG © Rubin Landau, CPUG 1. CP Education = ? O.Y. MultiDisciplinary Problem Solving Learn by doing, individual Projects  “Theory of CP” (grad, math) CS + Math + Physics in context More efficient, effective; Stimulate OK  “physics” time

5. Texas Tech Oct 09 © Rubin Landau, CPUG © Rubin Landau, CPUG 2. Why Need  (Phys Ed)?  how do, science =?  Historical rapid  how do, science =?     Ph Ed: C > delivery tool; Ph Ed+Research  Computing (math) too important to leave to CS  CSE Toolset, Compt Sci Thinking = freedom  “We are teaching the same things we taught 50 years ago” (APS/AAPT Taskforce on Grad Ed., R Diehl)  PH(t) narrows; CFD, MD, NLinear, QCD, Astro, Multi-scale, -PH  PH power = solve problem: basic prin’s, math; now + C

6. Texas Tech Oct 09 © Rubin Landau, CPUG © Rubin Landau, CPUG 2. Evidence Need  (Physics Ed) S, M, E Software

7. Texas Tech Oct 09 2. Need to Change Status Quo? © Rubin Landau, OSU  If work paradigm changes, education paradigm changes. Steve Stevenson, Clemson  For every complex problem there is an answer that is clear, simple, and wrong. (C as tool in Ed = CP) H. L. Mencken  You never change something by fighting the existing reality. To change something, build a new model that makes the existing model obsolete. Buckminster Fuller

8. Texas Tech Oct 09 © Rubin Landau, CPUG © Rubin Landau, CPUG  ≠ bad thing!   UG P overemphasize P = weaken  # STEM BS ≠ issue!  Death of Distance 2. What Do Physics Grads Do?

9. Texas Tech Oct 09 3. Elements of CP Education

10. Texas Tech Oct 09

11. 3. Elements of CP Education

12. © Rubin Landau, OSU © Rubin Landau, OSU 4. How: Computational Programs What's in a name? That which we call a rose By any other name would smell as sweet  Goru; All politics local  Goru; All politics local Use computers as tool in  Courses= good, CP Use computers as tool in  Courses= good,  CP Computational examples for courses > demos Computational examples for courses > demos Need understand computational thinking & tools Need understand computational thinking & tools Easy expect 1 course teach entire subject Easy expect 1 course teach entire subject (1>0) 5-6 US Undergraduate Degree Programs 5-6 US Undergraduate Degree Programs 25 Minor, Concentration, Track, Emphasis, Option, … 25 Minor, Concentration, Track, Emphasis, Option, … Grad easier,  constraints Grad easier,  constraints Maybe best: new curiculum + new texts Maybe best: new curiculum + new texts (NY Botanical)

13. Texas Tech Oct 09

14. We want your texts!

15. Texas Tech Oct 09 © Rubin Landau, CPUG © Rubin Landau, CPUG 5. e.g. eText Book: Survey of CP + PY  Web technologies natural for education + computing  Vision: ebook (MathML  pdf)  Now hot topic  Technology catching up  Tablet PC, eReaders  Live/search eqns, figs  Lectures (flash!), animations  Run codes, applets  Compadre (free)  Nat Science Digital Libe (free)  Princeton U Press: 1 st (≤ $)

16. Texas Tech Oct 09 6.Take Home Lessons  Computing essential forefront research  CP: bring real-world problems into classrooms  Multiscale, Multiphysics, multidisciplinary teams  Rejuvenate Physics Ed + Modern Research & Tools  Ph + CS + Math in problem/research context  Learn all 3 better, frees time for C, App Math  Education Including Research = best www.physics.oregonstate.edu/~rubin

17. Texas Tech Oct 09 © Rubin Landau, CPUG © Rubin Landau, CPUG Two Lower-Division Courses

18. Texas Tech Oct 09 © Rubin Landau, CPUG © Rubin Landau, CPUG Contents of Upper-Division Courses