1.0 Definition of Simulation and
Modelling in Teaching and Learning
1.1 Simulation
A
simulation of a system is the operation of a model, which is a representation
of that system. The model is amenable to manipulation which would be
impossible, too expensive, or too impractical to perform on the system which it
portrays. The operation of the model can be studied, and, from this, properties
concerning the behaviour of the actual system can be inferred.
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 behaviour of the real world system. As an example, behaviour 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.
Simulations are a useful teaching
strategy for illustrating a complex and changing situation. In a simulation,
the learner acts, the simulation reacts, the learner learns from this feedback.
Computer simulations can exemplify scientific concepts and situations thereby
allowing students to explore the nature of things. Issues such as cost, safety,
scope, time and scale can be overcome by the use of a scientific simulation.
Computer simulations help visual learners understand problems that they would
not thoroughly understand simply through reading about them or solving word
problems. The sophistication and variety of computer simulations in the field
of science is increasing rapidly.
1.2 Modeling
Modeling
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.
In
science, a model is the outcome of representing an object, phenomenon or idea
(the target) with a more familiar one (the source) (Tregidgo & Ratcliffe,
2000). There are two categories of models which are mental and conceptual. In
conceptual model, it consist of mathematical
models, computer models, and physical models. 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).
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).
A mathematical model is the use of mathematical language to describe the
behaviour of a system. It is a description or summarization of important
features of a real-world system or phenomenon in terms of symbols, equations,
and numbers. As a teacher that teaching Science, conceptual model is suitable
in classroom.
2.0 Theory of Simulation
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 is known as role taking or perspective.
In Piaget’s theory, it 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 that 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 behaviour. One then lets one’s decision that 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.
3.0 Differences Between Simulation
and Modeling
Both
computer modeling and simulations are computer applications which represent a
real world or imaginary system. Next, both computer modeling and simulations
help designers to save time and money. 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 results by doing impossible (in real world) changes. A model can be
considered as a static and a simulation can be considered as dynamic as the
variables of a simulation get always changed.
A model is a representation (usually on a smaller
scale) of some operating system or construct. It allows the user to predict how
changes in that system would affect other parts of the system or operation.
Simulation however, is the operation of the model of the system to evaluate the
performance of the system. It allows the user to optimize the system, to prevent
failure and to adjust any parameters within the system being investigated.
In this modern age of powerful
computers, it often makes no sense to put pencil to paper like in the old
days. Now, new software can perform
repetitive chemical engineering calculations in a fraction of the time it takes
to execute them by hand. Using a
simulation as a teaching or evaluating method can be considered whenever the
curricular material can be learned or student learning of prerequisite material
can be evaluated, through their participation in a mock real world situation in
which their choice of actions determines the outcome of the situation.
Instructional simulations have the potential to engage students in "deep
learning" that empowers understanding as opposed to "surface
learning" that requires only memorization.
Deep learning means student can learn scientific
methods including the importance of model building. Experiments and simulations are the way scientists do
their work. Using instructional simulations gives students concrete formats of what
it means to think like a scientist and do scientific work. Besides that, this
scientific method also teach the
relationships among variables in a model or models. Simulation allows students
to change parameter values and see what happens. Students also develop a feel for what variables are
important and the significance of magnitude changes in parameters.
Simulations
help students to understand probability and sampling theory. Instructional simulations have proven their
worth many times over in the statistics based fields. The ability to match simulation results with
an analytically derived conclusion is especially valuable in beginning classes,
where students often struggle with sampling theory.
Besides that,
deep learning also means student can learn to reflect on and extend knowledge. This
can be achieve by actively engaging in student-student or instructor-student
conversations needed to conduct a simulation. Instructional simulations by
their very nature cannot be passive learning. In this situation, students are
active participants in selecting parameter values, anticipating outcomes, and
formulating new questions to ask.
5.0 When To Use Modeling 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 (for example 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.
Besides
that, it also can be used to study about the system that is dangerous or
destructive. For instance the atom bomb, atomic reactor and missile launching. It is Impossible to
observe or influence these system. So, simulation can be used as a tool for
learning process.
6.0
Concept of Experiment in Modeling and Simulation
A
simulation experiment is a test or a series of tests in which meaningful
changes are made to the input variables
of a simulation model so that we may observe and identify the reasons for
changes in the performance measures. The number of experiments in a simulation
study is greater than or equal to the number of questions being asked about the
model.
Defining an experiment starts with deciding what you
want to know about your system or the questions you want to ask. For systems,
you usually want to quantify one or more performance metrics like system gain, rise
time, overshoot, delay and others. The next step is determining which design
parameters contribute most to performance variations. You might argue that a
key reason for running design experiments is figuring out which parameters to weak
in order to get a desired performance and you can certainly create experiments
to do just that. But knowing ahead of time the list of key design parameters
improves your experiment efficiency. Knowing the performance metrics you want
to focus on, and the parameters that most affect the metric, defines both the
analysis and measurements you need to run. You are now ready to setup your
experiments.
Simulation experiments are no less instructive, but
are typically best run using a structured and organized methodology. Once you
finish your system model, to get the most out of simulation experiments you
need to manage what design factors to consider, what analyses to run, and what
performance metrics you want to analyze. With these and other options, it is
easy to see that your experiment matrix can get pretty complicated. So a way to
manage simulation experiments is important.
7.0 Constructivist Lesson That Integrates Modeling and
Simulation
7.1 Introduction to Stella
STELLA software is one
example of modelling and simulation in Science. STELLA is a flexible computer
modelling package with an easy, intuitive interface that allows users to
construct dynamic models that realistically simulate biological systems. Given
the combination of ease of use and modelling power, the STELLA system is ideal
to interface with student investigative experiences. In its most basic form,
modelling in STELLA proceeds in three steps which are constructing a
qualitative model, parameterizing it, and exploring the model's dynamics.
There
are many sample of experiment that are contained in this software. The sample that I choose by using
STELLA is sample nitrogen cycle.
7.2 Concept of 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. In short, nitrogen cycle
involve several process. Firstly, humification process. Secondly,
mineralization process. Next, nitrogen biomass and last but not least is
fixation production.
The
process of ‘humification’ can occur naturally in soil,
or in the production of compost.
The importance of chemically stable humus is thought by some to be the fertility it provides to soils
in both a physical and chemical sense, though some agricultural experts put a greater
focus on other features of it, such as its ability to suppress disease. It
helps the soil retain moisture
by increasing microporosity,
and encourages the formation of good soil structure.
The incorporation of oxygen
into large organic molecular assemblages generates many active, negatively charged
sites that bind to positively charged ions
(cations)
of plant nutrients, making them more
available to the plant by way of ion exchange.
Humus allows soil organisms to feed and reproduce, and is often described as
the "life-force" of the soil. Yet, it is difficult to define humus
precisely; it is a highly complex substance, which is still not fully
understood.
Humus
should be differentiated from decomposing organic matter in that the latter is
rough-looking material, with the original plant remains still visible, whereas
fully humified organic matter is uniform in appearance (a dark, spongy,
jelly-like substance) and amorphous in structure, and may remain such for
millennia or more. It has no determinate shape, structure or character. However,
humified organic matter, when examined under the microscope may reveal tiny
plant, animal or microbial remains that have been mechanically, but not
chemically, degraded. This suggests a fuzzy boundary between humus and organic
matter. In most literature, humus is clearly considered as an integral part of soil organic matter.
Mineralization
is the process by which microbes decompose organic nitrogen from manure, organic
matter and crop residues to ammonium. As
it is a biological process, rates of mineralization vary with soil
temperature, moisture and the amount of oxygen in the soil (aeration).
7.2.1 Experiment of Nitrogen Cycle by
Using STELLA Software
The
experiment was ran by using STELLA. By using this software, we can change any
parameters that related to our experiment. There are four parameters that are
includes in this experiment software which are humification fraction,
mineralization fraction, nitrogen per unit biomass and fixation productivity
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At the beginning of experiment, we run the
normal mode. All the values of all parameters were fixed in normal condition
which act as a control for this experiment. We are not change any values in
parameter as it will be referred to the next experiment. After the button ‘run’
was clicked, the result was appeared as in the graph above. Students can do the
data analysis by refer to the graph.
From
the graph 1, when the value of the humification fraction is 0.2500 and
mineralization fraction value is 0.0500, all the values of nitrogen in humus
(humus refers to any organic matter that has reached a point of stability,
where it will break down no further and might, if conditions do not change,
remain as it is for centuries, if not millennia.) nitrogen in organic matter,
nitrogen in biomass and available nitrogen constant for each time. This happen
because the organic matter becomes the limiting factor.
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If
we want to investigate the relationship between the humification and nitrogen
cycle, the parameter of humification fraction must be changed. The data was
reset by click the ‘reset’ button. Then, the value of humification fraction was increased to half
of the population, 0.5333 while the values of other parameters remain
unchanged. After adjust the value of parameter, the button ‘run’ was clicked to
get the result.
From
graph 2, when the humification fraction increased, the nitrogen in humus also
increased and the value of nitrogen in organic matter becomes decreased. This
is because, when the process of humification increased, the amount of organic
matter transformed into humus become greater. Hence, the nitrogen in organic
matter reduced due to a large number of nitrogen in organic matter converted
into humus.
The
values of nitrogen in organic matter is
higher than nitrogen in biomass in time 0.00 to 5.00. After time 5.00,
both values were same and constant. While, the values of nitrogen available
were lowest than others. At a certain time, all the values of parameters become
constant due to limiting factor of organic matter.
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In
the next experiment, we adjust the parameter of humification fraction to the
highest value which is 0.9933 while the other parameters are remain unchanged.
After adjust the value, the button ‘run’ was clicked to see the result.
From
graph 3, when the humification fraction increased, the nitrogen in humus also increased higher than in the graph 1 and the value of
nitrogen in organic matter becomes decreased. This situation is same as in the
experiment 1 where when the process of humification increased, the amount of
organic matter transformed into humus become greater. Thus, the nitrogen in
organic matter reduced due to a large number of nitrogen in organic matter
converted into humus.
The
values of nitrogen in organic matter is lower than nitrogen in biomass in time
0.00 to 1.00. After time 1.00, both
values were still same and constant. While, the values of nitrogen available
were lowest than others. At a certain time, all the values of parameters become
constant due to limiting factor of organic matter.
 |
We
can also investigate the relationship between the mineralization fraction and
the nitrogen cycle. Before we run another experiment, the data must changed
back to normal value by click the button ‘reset’. After that, the parameter
mineralization fraction was manipulated. The value is increased to 0.0933 while
the other parameter will be the same. Then, the button ‘run’ was clicked to get
the result.
From
Graph 4, we can see when the mineralization fraction increased, the nitrogen in
organic matter increased and the value of nitrogen in humus decreased. This
happen because when the process of mineralization increased, more nitrogen in
organic matter will decomposed into ammonium. Therefore, the nitrogen in humus
reduced due to a large number of nitrogen in organic matter decomposed into
ammonium.
The
values of nitrogen in organic matter is higher than nitrogen in biomass in time
0.00 to 5.00. However, after time 5.00, both values become constant. While, the
values of nitrogen available were lowest than others. At a certain time, all
the values of parameters become constant due to limiting factor of organic
matter.
Based
on the experiment that was run, I can conclude that the values of humification
fraction and mineralization fraction are dependent on the amounts of organic
matter. Both process also very important that will affect the nitrogen cycle.
By using this STELLA software, we can get the result more accurate. Moreover,
it is more easy to plot the graph and adjust the parameter. It can also save
time and cost. We do not need to waste a lot of time when plot the graph like
manual.
8.0
Advantages and
Disadvantages of Simulation and Modeling in Teaching and Learning
Classroom
simulations motivate students by keeping them actively engaged in the learning
process through requiring that problem solving and decision is making skills be
used to make the simulation run. This technique is very interesting and easy to
understand. From this simulation and modelling, students can obtain a better
understanding of the system by observing the system's operation in detail over
long periods of time.
They can also 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.
Furthermore,
simulations provide a forum in which creative, divergent thinking is
legitimized and valued. Because simulations are much more like the “real world”
than many classroom methods, students do not stop learning when the class
period is over. Their interest carries over into informal out-of-class
discussions with other students and adults in which experiences and ideas are
shared and evaluated. Enthusiasm bubbles and school attendance is high.
Students become educational ambassadors as they continue their discussions at
home. Students describe this kind of learning as authentic and not boring.
Besides that, depending on the size of the model and
speed of the computer, a simulation can run many times faster than real time.
Consequently, results on system performance can be obtained in a matter of
minutes, maybe hours. This also has the advantage that results can be obtained
over a very long time frame, maybe years of operation, if required. Faster
experimentation also enables many ideas to be explored in a short time frame.
However, the software of simulation is expensive so
initial costs are high. There are free software packages though. Hence, not
every student can get the opportunity to explore this software. Some people
also may not like working on computer system. Students may not relate science
concept to microscopic and symbolic system representation. This simulation
modelling also can reduced the practice of graphing skills. Process of learning curve to use simulation packages may
be longer than the time available.
9.0 Conclusion :
Computer
simulations have the potential to enhance the way we teach and our students
learn. They allow us to bring even the most abstract concepts to life for our
students and incorporate otherwise impossible or impractical experiences into
our daily instruction. For me, this STELLA software was very
useful for me as a future teacher. When used in
conjunction with the guidelines presented here, students will be engaged in
inquiry, further develop their knowledge and conceptual understanding of the
content, gain meaningful practice with scientific process skills, and confront
their misconceptions. Additionally, they will gain scientific habits-of-mind
(such as the ability to visualize, contemplate, and explain complex concepts
and phenomena) that are both encouraged in the recent reform documents and
necessary for future careers in science.
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