[CAnet - news] Using computational scince and networks to teach basic science

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[An excellent article and web site on how new computational tools and
networks can help open up the world of science to students and educators.
The new tools of computational science, service oriented architectures and
networks can open up avenues of exploration for students in the context of a
scientific model that is implemented on the computer. Distributed computing
projects like Quarknet, Planetary Quest and many others also allow students
to work with real data and even in some cases contribute to new scientific
discoveries. Some excerpts from HPCwire article -- BSA] 


www.hpcwire.com

Let's Remember: The Noun is 'Science'......................
by Dr. Robert Panoff, Shodor Foundation       

[....]  
  
  Dan Warner, a professor of mathematical sciences at Clemson University 
  and one of the co-founders of Shodor, a national resource in 
  computational science education, recently put the situation very 
  clearly. In considering the vast oceans of data that are being 
  generated by a variety of observational laboratories, he observed, "It 
  isn't whether we have more chips processing the data, but whether we 
  have more neurons. We need many more people engaged in the conduct of 
  science, and computational science is a wonderful way to bring people 
  into science."
  
  Our challenge is to see that computational science education is a most 
  effective means for addressing a larger issue: quantitative reasoning. 
  In simple terms, we still have to ensure our children actually grow up 
  knowing how to compare quantities, even if it isn't being tested 
  anymore by the SAT! .....we have found that computational approaches to
science  education (the effective use of computational tools and
visualization   to teach the concepts of math and science) is as important
or more   important to stress as education in "computational science"
education   (teaching the process of building and testing a numerical
model). 
  
  ....Our computational science 
  classes at Shodor for middle school and high school students 
  (<http://www.shodor.org/succeed>) are in full gear now, and the 
  students learn everything from systems dynamics to agent-based 
  modeling, data analysis, and visualization. But the focus is not the 
  computer, but what the computer can help one learn about the world. 
  Students want to focus more on content driven disciplines. And that is 
  the strength of computational science, because modern math and science 
  are more about pattern recognition and characterization than mere 
  symbol manipulation. The tools of computational science can open up 
  avenues of exploration for students in ways that even direct 
  observation can't. The observation is paramount, but the observation 
  is made in the context of a scientific model that is implemented on 
  the computer. The science is at the heart of computational science.
  
   
  One approach to bring computational science to the masses is by 
  enlisting the help of many to assist in the task of analyzing the 
  overwhelming data being generated by a number of space and land-based 
  projects, from star surveys to earthquakes, from census data to 
  on-line archives of historical records. By incorporating the 
  exploration of real data -and there is so much of it yet to be 
  explored- as part of the learning of math and science starting in the 
  middle grades through high school and college, we can make education 
  an adventure for the whole human race. Unfortunately, we have many 
  math and science teachers at the elementary and middle school levels 
  who choose to be teachers at this level because they "don't do math!" 
  Significant work to incorporate models and computational tools into 
  the math education of many students has started to show its benefits, 
  by easing some of the math anxiety and showing how the math makes 
  sense. Some materials also show how to seamlessly incorporate these 
  tools into existing curricula in support of standards (see: 
  <http://www.shodor.org/interactivate>). For these approaches to become 
  more widespread, it will take a wholesale change in schools of 
  education in the pre-service preparation of math and science teachers, 
  which means a massive change in the attitudes of faculty in the 
  sciences and in education. 
  
  Computational science is both content and method. Students should know 
  the basics of the tools of computation, but also use computation to 
  learn the basics of chemistry, biology, physics, and engineering. So 
  many of the texts in use at all levels are wholly lacking. At the very 
  least, they fail to accurately communicate that much of what we know 
  in the sciences is from computational models as much as from direct 
  observation. 
  
  
  
   So, back to reality. If we keep thinking that computational science is 
  only for the biggest problems, then it affects only a few who would be 
  given limited access to limited resources concentrated in a few 
  national centers. If that is the only way that "real science" will get 
  done, we will never convince a doubting Congress the second time 
  around, let alone an administration that may not realize that only one 
  of the three R's actually begins with "R," of the relevance of 
  computational science. To justify an appropriate appropriation for a 
  long-range road map, we have to have a more wide-reaching goal of 
  computational science for everyone at all levels, and that means 
  developing an effective computational approach to science education as 
  well as an effective education in computational science.
  
  
  =================================================
  
  
  HPCwire contributor Dr. Robert M. Panoff is founder and Executive 
  Director of The Shodor Education Foundation, Inc., a non-profit 
  education and research corporation dedicated to reform and improvement 
  of mathematics and science education by appropriate incorporation of 
  computational and communication technologies. 
  
  He has been a consultant at several national laboratories and is a 
  frequent presenter at NSF-sponsored workshops on visualization, 
  supercomputing, and networking. He has served on the advisory panel 
  for Applications of Advanced Technology program at NSF, and is a 
  founding partner of NSF-affiliated Corporate and Foundation Alliance. 
  
  Dr. Panoff received his B.S. in physics from the University of Notre 
  Dame and his M.A. and Ph.D. in theoretical physics from Washington 
  University in St. Louis, undertaking both pre- and postdoctoral work 
  at the Courant Institute of Mathematical Sciences at New York 
  University.
  
  
  



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