1.0
Definition of Modelling and Simulation
1.1 What is modelling and simulation?
Modelling
is the process of producing a model; a model is a representation of the
construction and working of some system of interest. A model is similar to but
simpler than the system it represents. One purpose of a model is to enable the
analyst to predict the effect of changes to the system. On the one hand, a
model should be a close approximation to the real system and incorporate most
of its salient features. On the other hand, it should not be so complex that it
is impossible to understand and experiment with it. A good model is a judicious
trade off between realism and simplicity. Simulation practitioners recommend
increasing the complexity of a model iteratively. An important issue in modelling
is model validity. Model validation techniques include simulating the model
under known input conditions and comparing model output with system output.
Generally, a model intended for a simulation study is a mathematical model
developed with the help of simulation software. Mathematical model
classifications include deterministic (input and output variables are fixed
values) or stochastic (at least one of the input or output variables is
probabilistic); static (time is not taken into account) or dynamic
(time-varying interactions among variables are taken into account). Typically,
simulation models are stochastic and dynamic.
1.1.1 Model in science education
There
are different types of models in science. To categorize them, we should look
into what makes them be considered different. For this reason, we should
understand the difference between conceptual
and mental models. Conceptual models are devised as tools for the understanding
or teaching of systems. Based on the literature, conceptual models include mathematical
models, computer models, and physical models which are discussed in the
following sections. In addition to these models, there is another model called
“physics model” by the physics- education community. Mental models are
psychological representations of real or imaginary situations. They occur in a
person’s mind as that person perceives and conceptualizes the situations
happening in the world (Franco & Colinvaux, 2000). Norman (1983) indicates that
mental models are related to what people have in their heads and what guides
them using these things in their minds. In order to understand mental models,
their characteristics should be considered. A
conceptual model is
an external representation created
by teachers, or scientists that facilitates the comprehension or the teaching
of systems or states of affairs in the world (Greca & Moreire, 2000 and Wu
et al., 1998). According to Norman (1983), conceptual models are external
representations that are shared by a given community, and have their coherence with
the scientific knowledge of that community. These external representations can be mathematical formulations,
analogies, graphs, or material objects. A mathematical model is the use of mathematical
language to describe the behavior of a system. That is, it is a description or
summarization of important features of a real-world system or phenomenon in terms
of symbols, equations, and numbers. Mathematical models are approximations. A
computer model is a computer program which attempts to simulate the behavior of
a particular system. In other words, a computer model is a computer program
which is created by using a mathematical model to find analytical solutions to
problems which enable the prediction of the behavior of the complex system from
a set of parameters and initial conditions.Computer models allow students to
develop numerical models of the real world. The software is called a modeling
system or simulation language (Holland, 1988). Such computer simulations make
it possible for students to analyze complex systems. Sometimes, complex systems
require really very sophisticated mathematics to analyze and they cannot be analyzed
without computers (Chabay & Sherwood, 1999). Computer simulations may employ
many representations such as pictures, two-dimensional or three-dimensional animations,
graphs, vectors, and numerical data displays which are helpful in understanding
the concepts (Sherer et al., 2000).
1.1.2 Simulation
The
meaning of the term simulation changed after World War II, as the definition
given by the Oxford English Dictionary (fourth edition 1989) reflects: “The
technique of imitating the behaviour of some situation or process by means of a
suitable analogous situation or apparatus, especially for the purpose of study,
or the training of personnel.” In contemporary life, however , simulation has
generally come to be equated with science and technology and is viewed as
synonymous with computation and the digital computer. A simulation is the
manipulation of a model in such a way that it operates on time or space to
compress it, thus enabling one to perceive the interaction that would not
otherwise be apparent because of their
separation in time or space.
A
simulation of a system is the operation of a model of the system. The model can
be reconfigured and experimented with; usually, this is impossible, too
expensive or impractical to do in the system it represents. The operation of
the model can be studied, and hence, properties concerning the behaviour of the
actual system or its subsystem can be inferred. In its broadest sense,
simulation is a tool to evaluate the performance of a system, existing or
proposed, under different configurations of interest and over long periods of
real time. Simulation is used before an existing system is altered or a new
system built, to reduce the chances of failure to meet specifications, to
eliminate unforeseen bottlenecks, to prevent under or over-utilization of resources,
and to optimize system performance. Modelling and simulation is a discipline
for developing a level of understanding of the interaction of the part of a
system , and of the system as a whole. The level of understanding which may be
developed via this discipline is seldom achievable via any other discipline.
1.1.3 Theory of simulation
The
history of simulation theory reaches back quite far. Simulation (or empathy)
has roots in Dilthey’s Verstehen methodology and Goldman (unpublished) argues
that the great philosophers Hume and especially Kant had strong simulationist
learnings. Similarly, Perner & Howes (1992) describe that simulation is an
old idea in developmental psychology circles which has great importance in
Piaget’s psychology. In particular, simulation – known as role-taking or
perspective-taking in Piaget’s theory – helps young children overcome their
egocentric views. According to Fuller (1995), simulation and empathy was
“killed and buried” by the positivists. They distinguished between the context
of discovery and the context of justification and claimed that empathy only
belonged to the former context. While simulation can be used as a great
heuristic device to suggest predictive and explanatory hypotheses, it cannot be
used to justify these hypotheses – formulation and testing of generalizations
have to be done for a proper justification. However, empathy and simulation have
been resurrected in the last few decades. Putnam (cited in Fuller, 1995, p.
19), for example, argues that empathy plays a role in justification of
hypotheses because it “gives plausibility”.
Simulation
theory (ST) today has a strong influence on the philosophy of mind debate. ST
suggests that we do not understand others through the use of a folk
psychological theory. Rather, we use our own mental apparatus to form
predictions and explanations of someone by putting ourselves in the shoes of
another person and simulating them. ST is often described as off-line simulation,
although there are philosophers who maintain that off-line simulation is
only an ancillary hypothesis of ST (see Davies & Stone, 1995a, p. 4). In off-line simulation, one
takes one’s own decision-making system off-line
and supplies it with pretend inputs of beliefs and desires of the person one
wishes to simulate in order to predict their behavior. One then lets one’s
decision-making system do the work and come to a prediction. There are many
variants of ST, some differing
more than others. While some philosophers suggest a hybrid theory of TT and ST,
others argue that ST should replace the predominant TT. Gordon, for example, who
holds some of the strongest claims, suggests that simulation is fundamental to
the mastery of psychological concepts and that it has ramifications for the
ontology of psychological states (Fuller, 1995). While there are many varieties
and different views of ST, all
have in common that simulation acts as a very effective device for forming predictions and
explanations. This leads to an important implication of ST. Since simulation depends on one’s own mental apparatus, it is clear that ST (in contrast to TT)
is attributor dependent.
2.0 When to used modelling and
simulation?
Simulation
is used before an existing system is altered or a new system built, to reduce
the chances of failure to meet specifications, to eliminate unforeseen
bottlenecks, to prevent under or over-utilization of resources, and to optimize
system performance. A simulation generally refers to a computerized version of
the model which is run over time to study the implications of the defined
interactions. Simulations are generally iterative in there development. One
develops a model, simulates it, learns from the simulation, revises the model,
and continues the iterations until an adequate level of understanding is
developed.
Simulation
should be used when the consequences of a proposed action, plan or design
cannot be directly and immediately observed (i.e., the consequences are delayed
in time and/or dispersed in space) and/or it is simply impractical or
prohibitively expensive to test the alternatives directly.
3.0 Why use simulation and
modelling in teaching and learning?
Experiments
via simulation model have several important advantages versus physical
experiments. The advantage use
simulation and modelling it save time. In the real world evaluating the
long-term impact of process or design changes can take months or years. A
simulation model will inform your thinking in only minutes. By using this modelling and simulation it
also more accurate. Traditional computational mathematical methods require a
high degree of abstraction and do not account for important details. Simulation
modeling allows us to describe the structure of the system and its processes in
a natural way, without resorting to the use of formulas and strict mathematical
relationships. simulation model enables the visualization of the system over
time, animations illustrate the system in operation and graphical outputs
quantify the results. This allows us to visualize the resulting decision and
dramatically simplifies the task of bringing these ideas to client and
colleagues. Simulation allows us to solve problems in any area such as manufacturing, logistics, finance, health, and
many others and the most importance is in teaching and learning. In each case,
the model simulates real life and allows for a wide range of experiments with
no impact on real objects.
4.0 Differences between modelling
and simulation
A
simulation is changing one or more variables of a model and observing the
resulted changes. Although a model always tries to represent the actual system,
a simulation may try to observe the result by doing impossible (in real world)
changes. A model can be considered as a static and as simulation can be
considered as dynamics as the variable of a simulation get always changed. Simulation is a technique of
studying and analyzing the behaviour of a real world or an imaginary system by
mimicking it on a computer application. A simulation is works on a mathematical
model that describes the system. In a simulation, one or more variable of the
mathematical model is changed and resulted changes in other variables are
observed. Simulations enable users to predict the behavior of the real world
system. As an example, behavior of a ship can be simulated using a mathematical
model describes the governing laws of physics (fluid statistics and dynamics).
Users can change the variable such as speed, weight and observe the stability
of the ship.
5.0 How to run the experiment of modelling and simulation?
We
have used the STELLA software to run the experiment. We employ STELLA modelling
in combination with investigative exercises and experiments. The modelling helps students develop
hypotheses, explore predictions, summarize experimental results, and extend
their results to novel scenarios. STELLA
provides the flexibility to allow students to model a variety of experimental
systems and the power to provide for meaningful outcomes that relate to
specific biological content. We are developing and implementing modelling exercises
to bridge the rift between biological content and student experiments. Many of
the exercises will adapt a computer simulation software package that lets
students construct dynamic simulation models for their particular experiments.
The STELLA software allows students to develop and parameterize pool and flux
models to explore model dynamics, and to make quantitative predictions of
experimental results. The modelling exercises will not only help students
create more specific hypotheses, but they will also provide a context to
evaluate their experimental results.
5.1 Example of the sample modelling
and simulation that I choose from STELLA software is about simple Nitrogen Cycle.
5.1.1 Example experiment: Simple
Nitrogen cycle
The
main component of the nitrogen cycle starts with the element nitrogen in the
air. Two nitrogen oxides are found in the air as a result of interactions with
oxygen. Nitrogen will only react with oxygen in the presence of high
temperatures and pressures found near lightning bolts and in combustion
reactions in power plants or internal combustion engines. Nitric oxide, NO, and
nitrogen dioxide, NO2, are formed under these conditions. Eventually nitrogen
dioxide may react with water in rain to form nitric acid, HNO3. The nitrates
thus formed may be utilized by plants as a nutrient.
Nitrogen
in the air becomes a part of biological matter mostly through the actions of
bacteria and algae in a process known as nitrogen fixation. Legume plants such
as clover, alfalfa, and soybeans form nodules on the roots where nitrogen
fixing bacteria take nitrogen from the air and convert it into ammonia, NH3.
The ammonia is further converted by other bacteria first into nitrite ions,
NO2-, and then into nitrate ions, NO3-. Plants utilize the nitrate ions as a
nutrient or fertilizer for growth. Nitrogen is incorporate in many amino acids
which are further reacted to make proteins.
Ammonia
is also made through a synthetic process called the Haber Process. Nitrogen and
hydrogen are reacted under great pressure and temperature in the presence of a
catalyst to make ammonia. Ammonia may be directly applied to farm fields as
fertilizer. Ammonia may be further processed with oxygen to make nitric acid.
The reaction of ammonia and nitric acid produces ammonium nitrate which may
then be used as a fertilizer. Animal wastes when decomposed also return to the
earth as nitrates.
To
complete the cycle other bacteria in the soil carry out a process known as
denitrification which converts nitrates back to nitrogen gas. A side product of
this reaction is the production of a gas known as nitrous oxide, N2O. Nitrous
oxide, also known as "laughing gas" - mild anesthetic, is also a
greenhouse gas which contributes to global warming.
By
using STELLA I have choose the nitrogen cycle experiment. We can see before
running the experiment is several factors that effect the nitrogen cycle that
are humification fraction and mineralization fraction. The humification
fraction and mineralization fraction in the experiment are the variables that
we can manipulate. After we manipulated the value from one of the variable we
can see the changed in the amount of nitrogen cycle in organic matter and
nitrogen in humus. The nitrogen per humus and fixation productivity are fix
value. So, this is what we say it will make teaching and learning become
interesting. This will trigger the
thinking of the student to learn more and increase understanding of the
students. There will be many question in their mind and want to know what will
happen to the amount of nitrogen after we manipulated the value of one of the
variable. For example , thus the amount of nitrogen will low or increase?
As
the control we run the normal first, and we got the graph as below.
Running 1: Normal run (controller)
In
this experiment humification and mineralization fraction are the main factors
that affect nitrogen cycle. I have run the experiment with different
humification fraction and mineralization fraction. But for the first run is
normal run or controller in this experiment. The nitrogen per unit biomass was
set at 0.1000 and fixation productivity value also set at 0.0001. The value of
the humification fraction is 0.2500 and the mineralization fraction is 0.0500.
From the graph, we can see that nitrogen in humus (any organic matter that has
reach a point of stability where it will break down no further and might, if
condition do not change, remains as for centuries, if not millennia) and
nitrogen in organic matter are constant trough time because organic matter
become limiting factor. From this normal graph, student also build their own
hypothesis from the variable after they observed the graph. This first run, act
as a controller because we do not change any variable. So, we will compare this
graph with another graph that we will change the variable.
Run 2: Humification set at 0.5000
For
run 2. I have manipulated one of the parameter in this experiment that is
humification fraction at 0.5000. We can see the different between the first run
and second run by comparing the graph. Student can see from the graph that the
nitrogen in humus increase but nitrogen in organic matter decrease. The student
will think, why this is happen. Humification is the process of transformation
of organic matter into humus. So, when the organic matter was transformed into
humus its will reduced the organic matter in the soil. Mineralization is the
process by which microbes decompose organic nitrogen from manure, organic
matter in the soil into ammonium. Because of that, the amount of nitrogen in
organic matter become lower. Its become constant as the organic matter was
totally consumed by both process. Student can slowly understand and curious to
know what will happen when we increased again the humification factor.
Run 3: Humification was set at
1.000
For
run 3. I have increased the value of humification fraction to 1.0000. From the
graph, students can see that when the
humification process increase the nitrogen in the humus also increase. This is
because the organic matter was used in the humification process to form humus.
At the same time, mineralization occur that used the nitrogen in the organic
matter to produce ammonia. So, the nitrogen in organic matter will become more
lower. Its become constant as the organic matter was totally consumed by both
process. This was same as the previous graph but there are the differences on
the graph. Student can compare this graph with the controller (run 1). They can
now how the manipulated variable effect the nitrogen cycle.
Run
4: Mineralization fraction set at 0.5000
For
run 4, I was manipulated another parameter which is mineralization process. I have
increased the value of mineralization fraction to 0.1000. From the graph, I can see that the nitrogen in
humus become lower . This is because, when
the process of mineralization increase more nitrogen in organic matter will be
used. The humification fraction lower. Then the organic matter cannot be
converted to form humus. The value of nitrogen in biomass increased. We try to
change another variable in order to know how it effect the nitrogen cycle.
Student also can predict what will happen next after they manipulated the
variable. They can compare all the graph and come out with their discussion.
Lastly, they will come out with the conclusion either their hypothesis is
accepted or not.
After
we run all four variables, student can conclude that, the process of
humification and mineralization is opposite to each other because both of the
process depend on organics matter. Both will affect the amount of nitrogen in
the cycle. From the experiment by using the modelling and simulation student
can also see on their own how both factor will effect the nitrogen cycle. So,
automatically they understand what the teacher want to teach them. This give
benefit for both teacher and students. This will motivate students to learnt
and explore more by using this modelling and simulation. We don’t have to carry
out the experiment in the lab but just use the modelling to run experiment and
get the data.
This
modelling and simulation should also help teachers to teach with technology
rather than to use computers for personal productivity. Teachers, especially,
need pedagogical content knowledge which refers to knowledge about how students
learn from materials infused with technology. Successful technology use and
effective learning for science teaching is dependent on the teachers’ knowledge
of the technology itself, and how a particular tool is best utilized for
particular purposes, classroom or laboratory settings, and students themselves
(Hennessy, 2006). Simulation encourages the student to interact with the
variables, understand their sensitivities and appreciate how a change in one
variable results in changes in other variables. However, we have shown here
that the success of simulations as effective learning tools is dependent on how
simulations are used. Finding ideal uses of technology in science instruction
remains an active research area, and the technology itself is a “moving
target,” as new projects emerge on a regular basis. As Chiocchio and Lafrenière
(2009) recommend, teamwork and technology are becoming important components of
PBL in academic settings but fostering computer-assisted teamwork is complex
and time consuming. Knowing how and when to intervene would prove useful.
The
main advantage of this technology supplies the learner an immediate feedback
and reinforcements from a computer. Lately, this type of instructions has made
such a progressive movement that the learner can interactively use the software
to help the understanding of a topic in science education. Simulation provides a
situation that cannot be experienced to a learner. Simulation encourages
students to understand a situation easily and can present dynamic
representations to complex relationships. However, computer simulation are not completely
a better instructional tool than other instructional tools, they are more
active and viable instruction approaches that
can influence content knowledge. It allows students to correctly solve
problems related to the experiments in a linear sequence. Another advantage of
computer simulated experiments is that students deal with data in a controlled
setting the data that can be obtained directly by computer and stored; and
students can change variables easily. These results lead students to understand
scientific concepts much more than conventional models.
Computer
simulation is related with ICT. So, there are also several disadvantage when
use this technique in teaching and learning. The first is, equipment are expensive
that is problem for poor school such as school at rural area. One of the
problems is that to use computer simulations in science course, both students
and teachers should believe in the effectiveness of computer usage, but some of
teachers and students seem to be reluctant to use this new technology. Generally,
science teachers mainly rely on textbooks and other supplementary resources,
such as lab manuals, and test books. Also, some of students and teachers do not
have enough information how computer simulations can be applied effectively.
Therefore, they need to have computer usage background. Another problem in
computer simulation applications, especially in lab classes, students cannot
feel the real hands-on experiments taste. We know that as possible as if
students sensory organs, such as hearing, seeing, and touching, participate to
learning activities, student learning achievement should be much better than
other types of teaching. On the other hand, computer simulations cannot give
some of these feelings, like touching. In this case, computer simulations are
somehow perceived impersonal but only machine by students. Some simulation
programs are the lack o f well preparation because some times students can not
understand how to use very complex simulations and simulation programs may not
fit the learning age of students. It is not suit the level of the students.
Finally,
research demonstrates that technological tools can enhance learning in science
and mathematics, in a PBL setting, since they allow more personalized and
project-oriented commitments (Linn et al., 2000). According to Hakkarainen
(2009), PBL offers a good model to support students’ knowledge and skills, and
students will benefit from learning with and about technology such as
computer-based simulations in science and mathematics instruction.
Nevertheless, effective incorporation of these technologies into the curriculum
has been controversial, difficult and demanding.
6.0 Conclusion
From
this sample experiment of the modelling
and simulation there are many advantages that I can get. This technique is
suitable to be used by the teacher in teaching and learning process because it
is very interesting and easy to understand. There are many benefits when we use
modelling and simulation. We can study the effects of certain informational, organizational,
environmental and policy changes on the operation of a system by altering the
system's model; this can be done without disrupting the real system and significantly
reduces the risk of experimenting with the real system. Applications of simulation
abound in the areas of government, defence, computer and communication systems,
manufacturing, transportation (air traffic control), health care, ecology and
environment, sociological and behavioral studies, biosciences, epidemiology,
services (bank teller scheduling), economics and business analysis. The most
importance thing is we can use this technique in teaching and learning.
References
Anu Maria(1997). Introduction to modelling
and simulation. Retrieved at November 10 from
2002/Introduction_to_Modeling_and_Simulation.pdf
chapter 13: Modelling and Simulation. Retrieved at November 11 from http://itme000.louisiana.edu/assign/ENGR%20515/SE%20Reading/modelling/sys%20engr.13_Modeling-and-Simulation.pdf
Dr. Sami SAHIN (2006). Computer Simulation in Science Education:
Implications for Distance Education. Retrieved at November 10 from
http://tojde.anadolu.edu.tr/tojde24/pdf/article_12.pdf
Karen Shanton and Alvin Golman (2010). Retrieved at November 10 from
http://cs.explorelearning.com/docs/tech_sec_science_chapter_3.pdf
Randy L. Bell and Lara K. Smetana. Using Computer Simulations to
Enhance
Science Teaching
and Learning. Retrieved at November 10 from
Wikipedia. Modeling and simulation. Retrieved at November 10 from http://en.wikipedia.org/wiki/Modeling_and_simulation
No comments:
Post a Comment