Scientific Models For Education

Effective implementation of the Scientific Method requires good tools as well as insight.
The precision of empirical questions and their answers depends on the available scientific
tools. Therefore, progress in science depends on the invention of powerful tools for
empirical and theoretical investigations.
· Scientific instruments extend the range and acuity of human perception
enormously, making it possible to discover or verify patterns of great subtlety.
· Mathematics (the Science of Patterns) has been created, in large part, to provide
precise conceptual tools and methods for constructing and analyzing refined
models of physical systems and processes.

Computers in Education - P.Sloot CERN School on Computing, Sopron, Hongary August 1994

Sloot,P.(1994). Lecture on Parallel Scientific Computing and Simulations. CERN school on computing, Sopron, Hongary August 1994



The use of computers in physics is becoming increasingly important. There is hardly any
modern experiment thinkable where computers do not play an essential role one way or another. If we take a liberal approach to the concept of experimenting then we can make a fundamental
distinction in the applicability of computers in physics. On the one hand computers are used in
experimental set-ups such as a measuring/controlling/data analyses device, inevitable to the
accurate measurement and data handling. On the other side we have the field where the computer is used to perform some simulation of a physical phenomenon. This is essentially the realm of
computational physics. One of the crucial components of that research field is the correct
abstraction of a physical phenomenon to a conceptual model and the translation into a
computational model that can be validated. This leads us to the notion of a computer experiment
where the model and the computer take the place of the 'classical' experimental set-up and where
simulation replaces the experiment as such.


Voices of Computing
The choir of engineers, mathematicians, and scientists who make up the bulk of
our field better represents computing..............

Computer Science Educators .....

Our goal as computer science educators should include teaching programming, in a way that is motivating. Our challenge is to motivate the teaching of programming, not throw it out. I've been accused of "watering down" computer science with my media computation. I don't believe that's true. I like to believe that we are inventing new ways of motivating students to program. Removing programming from computer science is not just "watering down" -- it's gutting the core of computer science.


Physics is a universal "vocabulary" and computing a universal "methodology" that
together underpin today's diverse scientific and engineering professions.

Computational model

It is reasonable to attempt to describe the process of teaching in terms of a computational model because it is known that Computational models can lead to improvement in our understanding of natural phenomena and the understanding may allow us to distinguish useful from useless action...............

Computational Physics.A Better Model for Physics Education?-Landau et.al 2008-Copublished by the IEEE CS and the AIP

Computational physics provides a broader, more balanced, and more flexible education
than a traditional ...Moreover, presenting physics within a scientific problem solving
paradigm is a more effective and efficient way to teach physics than the traditional
approach. In response, schools have started to develop computational–physics education, in
which the dash indicates a union of computation and physics on pretty much equal footing as individual courses or formal programs. Another response, which we can call computational physics–- education, views the computer as a tool to advance physics education, without questioning what goes on inside the black box.

Landau et. al 2008
Copublished by the IEEE CS and the AIP

COMPUTING IN SCIENCE & ENGINEERING

Computer simulations by Jan Tobochnik

Computer simulations provide a vehicle for understanding many of the key concepts in
statistical mechanics. Most simulations are based on either Monte Carlo or molecular dynamics
algorithms. In Monte Carlo simulations the configurations are sampled according
to the appropriate probability distribution, and averages are computed from these configurations.
One of the most influential Monte Carlo algorithms is the Metropolis algorithm
whose 50th anniversary was celebrated in 2003. This algorithm simulates a system at constant
temperature T, volume V , and number of particles N, that is, it samples configurations
in the canonical ensemble. We summarize it here so that the reader can see how it relates
to the demon algorithm which we will describe next...........