ASSIGNMENT 3

1. GROUP
MEMBERS
1) NAJAT BINTI MD MUHTAR (D20152071966)
2) NOOR NADIA BINTI ALIAS (D20152071973)
3) NOR ERLINA BINTI MOHD SAHI (D20152071991)
TITLE REPORT 3 : SIMULATION
DATE 11/4/2017
GROUP A
LECTURER EN. AZMI BIN IBRAHIM
FACULTY OF SCIENCE AND MATHEMATICS
BIOLOGY DEPARTMENT
SBI3013
INFORMATION AND COMMUNICATION
TECHNOLOGY IN BIOLOGY
REPORT 3: SIMULATION

2. Introduction
A simulation of a system is the operation of a model. The model can be reconfigured,
experimented, can be studied and properties concerning the behavior of the actual system can
be identified. Simulation is also an immitation of the operation of a real-world process or system
over time. It is mostly used tool or software to run the system. In this project we used computer
simulation which is Biosawit simulation and STELLA software to run the system.
Computer simulation is the discipline of designing a model of an actual or theoretical
physical system, executing the model on a digital computer, and analyzing the execution output.
Simulation embodies the principle of “learning by doing”. To learn about the system we must
first build a model of some sort and then operate the model. The use of simulation is an activity
that is as natural as a child who role plays. Children understand the world around them by
simulating (with toys and figurines) most of their interactions with other people, animals and
objects. As adults, we lose some of this childlike behavior but recapture it later on through
computer simulation. To understand reality and all of its complexity, we must build artificial
objects and dynamically act out roles with them. Computer simulation is the electronic
equivalent of this type of role playing and it serves to drive synthetic environments and virtual
worlds. Within the overall task of simulation, there are three primary sub-fields: model design,
model execution and model analysis. In this project, we choose biosawit as a simulation model
and STELLA software to run the system.
STELLA is programming language. It is short for Systems Thinking, Experimental
Learning Laboratory with Animation and also marketed as iThink. It is a visual programming
language for system dynamics modeling introduced by Barry Richmond in 1985. The program,
distributed by isee systems (formerly High Performance Systems) allows users to run models
created as graphical representations of a system using four fundamental building blocks.
STELLA has been used in academia as a teaching tool and has been utilized in a variety of
research and business applications. The program has received positive reviews, being praised
in particular for its ease of use and low cost. Because of its simplicity relative to more complex
modeling languages, STELLA has been cited as a useful tool in educational settings.

3. Aim of Simulation:
 To create a model which can represents a system.
 To make an experiment for understand behavior of system or evaluate different
strategies.
 To observe events, processes either properties or behaviors about system with model.
 To try different decisions and options
 To declare real life system with mathematics
How can be successful simulation model?
 Easy to understand
 Target direction
 Nearly real system
 Produce result faster
 Easy to control and operate
 Updateable
 Evolutionary
 Effective report
When simulators are used?
 If system is not available for making an experiment
 If the system is in during design phase
 If the system or problems are complex
 If system behavior is analyzed
 If computer is exist
Application:
 Education, military system, health, ministry and many others field.

4. Graph 1: Relationship between the number of palm, rat and owl population
Description: Graph 1 shows the relationship between palm source, rat and owl population.
Based on the graph plot, we can say that the population numbers of rats were depending on the
number of owl. The higher population of owls, the lower the population of rats. Rats are prey for
owl. While the number of rats give effect to the number of palm sources which are the higher the
number of rat, the lower the number of palm source produced. The palm is source of food for rat
and rat act as pest to palm ecosystem. So, we may conclude that all populations are depending
to each other and it is called food chain. To produce the higher number of palm sources, we
must increase the population of owl as a predator to the rat to control the palm ecosystem.
ORIGINAL GRAPH

5. Graph 2: Modified graph 1 (Relationship between the number of palm, rat and owl population)
Graph 3: Modified graph 2 (Relationship between the number of palm, rat and owl population)

6. Description: From the graph 2 and 3, they show the relationship between palm source, rat and
owl population. Based on these graph plots, we can say that the pattern of the graph is different
from the original graph. This is because it is the same concept with food chain. When the
population number of rat and owl are changed, it will give different result of palm sources. For
example, when the population number of rat increased, the number population of palm sources
will decreased. This is because of owl is decreased so that they do not eat all the rats.
Oppositely, it will cause the population number of palm sources increased.
Functions of simulation in future learning (Advantages)
The word ‘simulated’ means to imitate exactly. Interest is aroused in the students through ‘role
playing’ while teaching. This skill is used by teachers and students in the classroom by playing
some role without any preliminary training or any rehearsal. Simulation in teaching has recently
entered the field of education. It is used at different levels of instruction. The teacher is trained
practically and also imparted theoretical learning.
Computer simulations are computer-generated, dynamic models of the real world and its
processes and often represent theoretical or simplified versions of real-world components,
phenomena, or processes. As such, computer simulations offer an environment for students to
explore the phenomena of the real world and better understand the science behind the
phenomena. A large body of literature exists on computer simulations in science education.
Ideally, computer simulations are flexible, dynamic, and interactive and thus encourage inquiry-
based exploration, in which students draw their own conclusions about scientific concepts and
ideas by altering values of different variables and observing their effect.
Interactive computer simulations give students a sense of control and ownership of their
exploration and discovery, and thus, it enhances their understanding and retention of
information. These simulations offer the opportunity to re-create and visualize phenomena of the
real world that would take too long (e.g., geologic processes) or might be too dangerous or too
complicated for a conventional classroom/laboratory setting. Simulations also allow students to
focus on the essential aspects of a process or system while eliminating extraneous variables,
promoting understanding of the causal relationships between events or variables (de Jong and
van Joolingen, 1998). The learning-by-doing approach can also make abstract concepts more
concrete (Ramasundarm et al., 2005). The interactive engagement and immediate feedback of
simulations allow students to work at their own pace and easily repeat trials and thus promote
conceptual reasoning and deeper understanding.

7. Disadvantages of computer simulations
Some previous studies also reported mixed or inconclusive results on the effect of simulations
on enhancing students’ learning (e.g., Anglin et al., 2004; Edsall and Wentz, 2007; Randy and
Trundle, 2008; Scalise et al., 2011). Researchers found that traditional methods are just as
effective and computer simulations alone are inadequate in helping students understand more-
complex ideas because these more-complex simulations often require higher interactivity, which
can potentially overwhelm students. Therefore, scaffolding is necessary to help students
develop enough background knowledge so that they are not overwhelmed but are adequately
equipped and ready to explore the phenomena in question.
Besides, We have a poor understanding of how some physical systems work. For this
reason it has not been possible to create simulations that can accurately predict the occurrence
and effects of earthquakes and tsunami. The formula and functions that are used may not
provide an accurate description of the system resulting in inaccurate output from the simulation.
Other than that, complex simulations require the use of a computer system with a fast processor
and large amounts of memory.

8. Conclusion
The use of computer-based technology in classrooms is now well established, especially
simulation tools that are freely available over the Internet, such as the PhET collection
developed by the University of Colorado. Both computer-based simulation and traditional paper-
based material can be effective at enhancing students’ understanding of the processes
involved. The advantages of using a simulation model, in terms of its potential to promote
higher-level thinking and should be leveraged in teaching students the difficult-to-master
concepts and processes. The benefits of using simulations are worth investing the time and
effort to develop the associated curricular materials. In the other hand, traditional paper-based
approaches should not be discarded because they are similarly effective for teaching concepts,
information, and terminology. In fact, scaffolding using traditional teaching approaches is
necessary to help students develop enough background knowledge so that they are ready to
explore within simulations.

9. References
Anglin, G.J., Vaez, H., Cunningham, K.L. (2004). The role of static and animated graphics. In
Jonassen, D.H., ed., Handbook of research for educational communications and technology,
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ed. Bloomington, IN: Association for Educational Communication & Technology, 865–
916.
Baillie, C. and Percoco G. (2000). A Study of Present Use and Usefulness of Computer-Based
Learning at a Technological University. European Journal of Engineering Education. 25, 33-
43.
de Jong, T., and van Joolingen, W. (1998). Scientific discovery learning with computer
simulations of conceptual domains. Review of Educational Research, 68(2):179–201.
Fatimah Lateef. (2010). Simulation-based learning: Just like the real thing. Journal of
Emergencies, Trauma, and Shock, 3(4): 348–352. Retrived from:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2966567/
Ramasundarm, V., Grunwald, S., Mangeot, A., Comerford, N.B.,and Bliss, C.M. (2005).
Development of an environmental virtual field laboratory Computers, 45(1):21–34.
Wehrli, G. & Nyquist, J.G. (2003). Teaching strategies/methodologies: advantages,
disadvantages/cautions, keys to success. Creating an Educational Curriculum for Learners at
Any Level. AABB Conference. Retrived from:
http://www2.tulane.edu/som/ome/upload/ComparisonOfTeachingMethodologies.pdf
Wei Luo,Jon Pelletier, Kirk Duffin, et al. (2016). Advantages of computer simulation in
enhancing students’ learning about landform evolution: a case study using the Grand
Canyon. Journal Of Geoscience Education, (64), 60–73. Retrived from: http://nagt-
jge.org/doi/pdf/10.5408/15-080.1?code=gete-site