This document discusses the use of STELLA simulation software to model various systems over time. It provides examples of using STELLA to model predator-prey relationships in an oil palm plantation and to predict climate change scenarios. The document also discusses how simulations like STELLA can encourage student interest and engagement in science learning by making experiments interactive, allowing hypothesis testing, and helping students understand real-world systems.

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FACULTY OF SCIENCE AND MATHEMATICS
SSI 3013
INFORMATION AND COMMUNICATION TECHNOLOGY IN
SCIENCE
ASSIGNMENT 3:
STELLA SIMULATOR
STUDENT NAME &
MATRIC NUMBER
1. NOR HAYATI BINTI ZAINUDDIN
D20151071002
2. NURUL SYAMIMI AUNI BINTI RAMLI
D20151071004
3. SAHIRAH AKILAH BINTI MOHD SAH
D20151071572
LECTURER’S NAME ENCIK AZMI BIN IBRAHIM
GROUP A

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CONTENT
NO. CONTENT PAGE
1. INTRODUCTION 3
2. MANIPULATE VARIABLE 4 – 6
3. PREDICTON 7 – 9
4. STUDENT MOTIVATION 9 - 10
5. CONCLUSION 10
6. REFERENCES 10

3. 3
1.0 INTRODUCTION
What is Simulation?
Simulation is the imitation of the operation, condition or situation of a real world process or
system over time. A simulation may be performed through solving a set of equations (a
mathematical model), constructing a physical model, staged rehearsal and games (computer
graphics model). Simulations are very useful tools that allow experimentation without
exposure to risk.
What is STELLA simulation?
STELLA simulation is computer simulation software that offers a practical way to visualize
on how the real situations work. This simulation is being used to stimulate a system over
time and enable students to explore the model and system creatively. They able to change the
value or variables of the system and the simulation will give the outcomes in form of graph.
Based on the graph, students will understand the topic easier.

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2.0 MANIPULATE VARIABLE
Example of STELLA using the oil Palm
Figure 1
For this figure, there are two parameter knob where pink in colour for RAT and blue in colour
for OWL. In this simulator, users can build they own models. Users can put the parameter based
on their experiments.
In this figure, it was known as normal because it parameter knob was set to its initial value. Plus,
there are also sliders for the values of ‘Buah Rosak’ dan ‘Buah Dituai’. These sliders can help
users for initiate the approximate values of the experiment. Next, for the graph, it consists three
lines that differ in colour to indicate the parameter. Pink for the amount of rat, red for oil palm
produced and blue for the amount of owl.

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Figure 2
In this figure, the both parameter knob was adjusted to be higher than normal. The graph
produced aslo changed due to parameter changed. The value for ‘Buah rosak’ and ‘Buah Dituai’
also changed. There is a rise in owl populations and causing the rat populations reach zero. As
the numbers of owls are decreasing, the rats populations are continuously increase because there
are no attack from the predator. This will resulting in the decreasing of palms productions.

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Figure 3
For this figure, there is only one parameter knob changed, it was increased than its normal value.
Regarding to the theory, if there is a rise in food sources, the number of predator will also
increase because there is sufficient amount of food sources. We can see clearly, the relationship
between numbers of owl and rat populations and the correlations between the prey and predators
with the palms productions years to years. Because of the numbers of prey-predators populations
have their own up and downs, the palms productions are not greatly influenced and continued to
grow.

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3.0 PREDICTION
The STELLA software was used to dynamically visualize the effects of one particular variable.
In this software, the main component is variables. It is simulated over the designated period of
time with tools that include stocks and flow diagrams, casual loops, model equations and built-in
functions.
STELLA software can be used to teach about equilibrium versus kinetic reactions. It allows one
to examine the implications of non-equilibrium systems. In this exercise, students use STELLA
to model the ground water contaminant system. They conceptualize the sorption system, build a
diagram to represent their conceptualization and convert their diagram to a quantitative and
executable model. The system is initially out of equilibrium and the students run it to
equilibrium. Throughout the process, students explore the systems and discover connections
among model parameters, initial conditions and resulting changes in concentrations.
Next, the STELLA system thinking software can also predict the climate change scenarios. A
simple simulation for water storage was developed for one of the biggest and most productive
basins in Mexico. According to Mendoza and Puche (2007), the analysis and evaluation of future
scenarios, even though they are uncertain, will allow early detection and the possibility of
adaptation and mitigation. Although severe, the case of water supply forecasts for the Rio Yaqui
Basin is one of the least problematic for the state of Sonora. The neighbouring watershed, the
Sonora River Basin, has had problems with water since 1980 and future climate scenarios for the
basin do not show a favourable projection. There is a government project in Sonora that aims to
transport water from the Rio Yaqui Basin to the Sonora River Basin due to high water demands
in the latter.

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i. Figure 3 shows the runoff resulting from the SRES A2 scenario. The periods of water
scarcity in severe although there are high water flows in some years.
ii. Figure 4 shows the storage projections show a positive trend but also some annual variations
iii. The historical scarcity periods of 1989, 1990, 2000, 2001, 2002 and 2003 were due to there
being less rain, but also resulted from bad water management. With this simulation, potential
storage deficiencies for 2034, 2070 and 2074 were identified.
Figure 2 shows the climate change
projections obtained from the CMIP3
dataset
Figure 1 shows the climate change simulation in
STELLA 9.0.2 with individual scripts for each
subbasin in the study area: Angostura, Oviachic, and
Novillo.

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iv. In contrast, in 2057 there is a storage projection value of 3,177 Hm
3
, which is close to the
maximum level that can be supported by the Oviachic Dam (3,226.7 Hm
3
).
4.0 STUDENT MOTIVATION
How Stella Simulation Can Encourage Student Interest in Learning Science in School?
Simulations have great potential to increase student interest in learning science. Besides that, it
also can improve science learning in classroom.
1) Teaching and learning process becomes more interesting.
When teachers using simulations in teaching Science subject, it will increase the
enjoyment and involvement of students in classroom. Besides, it will implement the
cooperative and constructivist learning among students in learning Science. When
students experience the interesting and fun simulation activities, this will enhance the
teaching and learning process of Science and at the same time increase student’s interest
in Science subject.
2) Students can predict the outcomes of the experiment.
The advance tools in simulations technology nowadays, able students to predict the
outcomes of the Science experiments. They manage to make hypotheses and conclusions
for the experiments. For example, by using STELLA simulation, students can study the
relationship of prey predator in palm oil plantation. Based on the graph that has been
automatically plotted in the STELLA simulation, they can make a prediction on
population of the prey predator in palm oil plantation.
3) Increase the understanding among the students.
Simulations allow for a deeper exploration of a concept or topic in Science subject.
Students able to thinking creatively and critically in understanding the experiments or
situations that will be learn using simulations. Thus, they able to understand the Science
topic easier when using simulation.

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4) Students can relate virtual experiences in simulations to what is happening in the
real world.
By using simulation in learning Science, students can see and feel the virtual situation of
what is happening in the real world. So that, they will more understand the topic and it
can increase their interest in learning that topic. For example, when students use the
Stella simulation in learning the relationship of prey predator, they will see the changes
on population of prey predator involved. They also able to predict the size of prey
predator using STELLA. Thus, it will be easier for them to understand the topic
encourage their interest in learning Science.
5.0 CONCLUSION
STELLA is used to help teach such as economics, climate change, chemistry, biology and
anatomy. It teaches students to think systemically, to gain a more profound understanding and to
make better decisions. With STELLA, students can map and simulate systems. Working
collaboratively or independently on projects, hands-on exploration and learning is made possible
with risk-free experiments through the use of knobs, switches, sliders, buttons and other input
devices to change variables and run scenarios. Results can be viewed instantly in tables and
graphs.
6.0 REFERENCE
Learning Science Through Computer Games and Simulations. (2011). Retrieved April 25, 2018,
from https://www.nap.edu/read/13078/chapter/5#64
Coffman, T. (2006). Using Simulations to Enhance Teaching and Learning: Encouraging the
Creative Process. Retrieved April 25, 2018, from
https://www.vste.org/documents/vj_2006_02.pdf
Levy, J. and Mayer, L., 1999, Systems Modeling of Nonequilibrium Chemical Reactions Using
STELLA, Journal of Geoscience Education, v. 47, p. 413
Use of Stella Software for the Modelling... (PDF Download Available). Available from:
https://www.researchgate.net/publication/264346976_Use_of_Stella_Software_for_the_
Modelling_of_Climate_Change_Impacts_on_Water_Balance_for_the_Rio_Yaqui_Basin
_Sonora_Mexico [accessed Apr 25 2018]