Here is what he had to say: I recently started a position as assistant professor of Computational Mathematics at Kean with the New Jersey Center for Science and Education Technology.. I
Trang 1Welcome to the inaugural issue of Computational Science Education Spotlight In every
issue of CSES, we ask an educator about how they are using computational science Some have integrated its use into their classes, while others have instigated an entire curriculum change or new concentration offered at their school This month we talked to David Joiner,
from Kean University Here is what he had to say:
I recently started a position as assistant professor of Computational Mathematics at Kean with the New Jersey Center for Science and Education Technology The program is a 5 year combined bachelors-masters degree program with separate tracks for both future teachers and researchers Kean is the largest producer of public school teachers in New Jersey, and we hope to be the largest producer of science and math teachers as well Our goal is that students in our program will see computation as a natural part
of doing math and science, and go on to use it in the classroom when they become teachers
What is "computational science"? The answer I hear a lot is "using computers to do science",
a statement vague enough that it can mean everything to those who already know the answer while meaning nothing to those who do not I see modern computational science as having three key areas The first is the one most people think of: modeling and simulation But equally important in terms of modern applications are the areas of visualization and data mining and analysis
Modeling and simulation is in many respects not that new There has always been the cycle
in science between observation, theory, and prediction You see something happening in the world, you think about what might be making it happen, you predict what would happen in a different situation, you set up an experiment, and you are back at the beginning of the cycle again Modeling and simulation is all about the prediction phase of the scientific method, going from what you think you know to what you think you will observe
The big difference between now and thirty years ago is the shift in where you cut corners It used to be that all of the approximation was done in the theory, with great effort being taken to find a way to use reasonable assumptions to turn an exact description of nature, which could not be solved, into an approximate description of nature that could be solved With modern computing tools, the corners cut are in the solution of the problem, rather than
in setting up the problem Numerical integration techniques replace closed form integrals Monte Carlo techniques replace transforms of probability distributions Local rules replace global laws On the down side, you have to settle for a numerical solution, but on the up side, you don't have to make as many assumptions in your descriptions of natural
phenomena
Data mining is a field that has really grown, largely due to the many projects out there that exist just to observe a single snapshot or measurement They are pushing the bounds of our abilities to store, organize, and retrieve information Look at the human genome project Huge amounts of data are being stored in an attempt to catalog the total genetic information
of a human being When you finally have the information stored, the work of analyzing it has just begun Genetics and bioinformatics have taken data retrieval and pattern recognition to new heights, rewriting our understanding of evolution In astronomy, the strides made by recent earth based telescope and space based survey missions are too many to be listed here from Hipparcos to the Sloan Digital Sky Survey, we are generating so much data that many observers need not even go to a telescope to get the data they need With projects like SIMBAD and the National Virtual Observatory, tools and
standards are being put into place to give everyone access
to top quality scientific data
In terms of visualization, changes in the power of computing
have helped us go from detailed graphs of a relation
between two variables, a tedious effort performed by
graduate students, to detailed graphs of relations between
multiple variables, a tedious effort performed by a
computer Advances in computer memory have made it
practical to create and store very high-resolution graphics
and advances in CPU power have made it practical to create
those graphics 3-D visualization and animation allow for
explorations of both data and model results that could not
have done before, looking for patterns that we previously
might have missed
Trang 2My background in computational science education began near the end of my graduate studies at Rensselaer Polytechnic Institute I got involved in a distance education project teaching an AP physics course to a class at a high school in Cobleskill, NY, which sparked my interest in not just education but also in the potential for technology to be a major factor in education Upon completing my degree, I moved to North Carolina and met up with Bob Panoff at the Shodor Education Foundation, where I started as a post-doc I began working
on a project to collect and create resources to help teach topics in science and math using computing, as well as to teach topics in computational science This project, the
Computational Science Education Reference Desk, was my main project at Shodor for the next five years, and I still work on the project as a faculty member at Kean We recently received funding from the National Science Foundation's National Science Digital Library Program to establish CSERD as one of the Pathway projects of the NSDL
In addition to working with the CSERD project, I was involved in teaching and developing workshops for another Shodor project, the National Computational Science Institute, an NSF Course, Curriculum, and Laboratory Improvement - National Dissemination project I, along with University of Northern Iowa professor Paul Gray, developed the curriculum for and piloted the Parallel Computing workshops for NCSI
I got my Ph.D in Physics from Rennselaer Polytechnic Institute in 1999 My research
interest was computational astrophysics, in particular the formation of and the optical
properties of interstellar dust My thesis research studied the formation of dust in nova shells When a nova explodes, the light from the star increases dramatically and then
gradually decreases over a period from weeks to months For many novae, as the ejecta cools, a large amount of carbon or silicate dust forms, obscuring the star, and as the dust clears, the star becomes visible again What I find interesting about the dust formation event in novae is that we have a chemical process being observed to happen in space over a period of weeks Normally in astronomy you get a snapshot, one frame out of an evolution that may take millions or billions of years Novae ejectas expand and cool over weeks You actually get to watch it happen You almost never get to do that in astronomy
Currently I am developing and teaching the first of a three semester series, Mathematical and Computational Methods of Science The series replaces Calculus I-II and introductory computer programming for students in the NJCSTE Students get to see calculus presented with both computation and application to science as key parts of the curriculum, and the content in the class is integrated with the students' science courses Students get to see practical applications of programming where they are using the computer to actually
compute
The course has a lab component which is divided roughly half and half into (a) a
"computers-on" experience turning the theory of calculus into programs that can be tested and reused and (b) science based activities where we try to make students see the use of mathematics as they are learning it, using visualization tools to look at complex molecules
or minimization techniques to study the probability distributions of particles or degenerate states
In addition to the heavy computing requirement in the Bachelor of Science and Technology Education that students in the NJCSTE program will receive, we are developing a Master of Science in Computational Mathematics, with an emphasis on computational algorithms and their application to problems in the science
I am very happy to be able to continue making CSERD a resource that more people can use
by becoming a partner in the National Science Digital Library There is a lot of effort going into making good computational learning objects A lot of good objects are not being found
Trang 3and disseminated A lot of excellent tools don't have the educational resources needed in order for teachers to be able to use them A lot of tools are out there that are just plain wrong There needs to be a community effort to find the good stuff, find out how to match
up models that don't have lessons with lessons that need models, and to filter out the stuff that isn't good I see CSERD as an attempt to build this community effort - not just
cataloging materials but leading an effort to review the materials as well
I'm also hoping that when our freshman class from the NJCSTE graduates in five years that I will be able to say that we are producing the teachers that are changing the way science and math are taught in New Jersey These will be the teachers who raise the standards, who show science and math as being natural partners, who show that technology can be used in the classroom to create information as well as to present it, and who show how modeling can add interactivity to science and math
I must give a nod to our administration at Kean From President Farahi on down at Kean, the NJCSTE has had nothing but support We have been given great flexibility and incentive to incorporate computing into courses in the center All incoming freshmen in the center were given new laptops, and the laptops had numerical software installed on the machines Our classroom and the students study room in the center have both wireless and wired internet,
as well as ample power outlets, available for all students, making it easy to use computing
in the classroom My favorite computational science resource is CSERD (cserd.nsdl.org), of course! There is a ton of stuff at Shodor - both that I have been involved in creating and those I have not - that I and other Shodorites are proud of and recommend I really like the
graphing tools Function Flyer and
Graphit, and use them daily in my undergraduate classes My students have real problems being able to "see" functions, and to easily remember them, so being able to show them interactively and to give my students
a tool to do the same is important I also am very fond of the MASTER tool
GalaxSee, partly because of my own work on the Windows versions, and
my own verification and validation testing of the integration routines in both the Mac and Windows versions GalaxSee comes with a large number
of lesson plans ranging from very introductory (Why doesn't the Earth fall into the Sun?) to very advanced (How
does the virial theorem describe bounds to
gravitational collapse?)
Of non-Shodor projects, my single favorite
example of using computing to teach science
is the tools set created by Chris Mihos at Case
Western Reserve University The Dynamical
Astronomy Javalab is great not just
because it reflects cutting edge research in an
easy to use classroom package, but because
it includes all of the lesson plans that a high
school teacher or undergraduate faculty
member would need to incorporate it into the
classroom
For teaching how to do computational
science, my favorite non-Shodor resource is the Bootable Cluster CD, created by Paul Gray
at the University of Northern Iowa The BCCD is a clustering environment designed to turn a Windows lab into a Linux cluster without ever touching the hard drive If you have DHCP and DNS such that machines get a fully qualified domain name with their DHCP assigned IP, you put the CDs in, hit return nine times, and you have a cluster After your classroom activities, you reboot the machines, remove the CDs, and the machines are exactly the way they were before you started Even if you don't have DHCP and DNS configured, there are BCCD
options to get around this It has been extensively tested (with help from NCSI funding) for most major video and network cards, and includes not just common clustering tools but also tools for studying networking
Trang 4My plans for the future of computational science are focused on changing the way that science and math are taught both in New Jersey public schools and across grade levels all over the country by training future teachers; by training the faculty who will teach future teachers; and by finding, cataloging, and making available the very best in computational science education resources
What advice do I give to people new to the field of computational science education? First,
to take advantage of the many opportunities to see what people are doing already The NCSI
workshops are a great resource, as is the education program at the supercomputing
conference series NCSI has been working with many other groups to provide workshops at the conferences that many new people are already going to - in particular there has been a great partnership between NCSI and Sigma Xi, and Sigma Xi members are encouraged to apply to Sigma Xi to host a NCSI workshop
There is a great community developing around computational science education, and we welcome anyone looking to learn more about how to use computing in education, or looking
to learn more about how to teach how to compute
- David Joiner
- Associate Professor, Computational Mathematics
- Department of Science and Technology Education
- Kean University
- 908-737-3427