.

1. SBI3013
INFORMATION AND COMMUNICATION TECHNOLOGY IN BIOLOGY
𝐒𝐓𝐄𝐋𝐋𝐀®
SIMULATION: POPULATION MODEL
Prepared by
NAME MATRIC NO GROUP
TIONG MING MING D20181082879 B
NUR ZAFIRAH BINTI ABD RAHMAN D20181082896 B
IVY CHRISTIE ANAK ENDAH D20181082931 B
MURSHIDA ADNI BINTI MUHD GHAZALI D20181082940 B
CLEARVIANA FRANKIE YONG D20181082942 B
LECTURER: ENCIK AZMI BIN IBRAHIM
DATE : 29/04/2021

2. INTRODUCTION
Computer simulation is defined as the use of technology devices to implement the model or
the dynamic responses in an animated sequence. In other words, a mathematical description or
model of a real system is applied in the form of a computer program. According to Tavasuria
et al. (2014), computer simulation is a highly effective as teaching and learning media
especially for science subjects. Basically, there are three different types of commonly uses
simulations which are live, virtual and constructive. In terms of live simulation, real people
involved together with the operating system which may also involve actual equipment. This
kind of simulation provides an identical operation. Next, virtual simulation is a technology to
simulate the true system with virtual system based on the computer technology by involving
real people controlling the simulated systems. The simulated individuals take part in operating
the simulated system. It provides the capability to analyze concepts, predict possible outcomes,
generate statistics and perform analysis.
Population is a group of identical organisms live together under the same geographical
area at the same time. Generally, a population composed a group of species which are able to
interbreed living together in an ecosystem. The members of a population will depend on the
same resources and environmental constraints. They also rely on the ability of other members
to persist over time. Within a population, age, sex and ethnicity are all basic elements. The
combination and transformation of information in age and sex into charts is known as
population pyramids which play important roles in investigating the population growth.
However, there are also other non-negligible population characteristics namely natality,
mortality, population size and population dispersion.

3. OBJECTIVE
The purpose of this study is to:
i) To provide an efficient way in understanding population in real life situations.
ii) To investigate how simulation technology plays its roles understanding topic
population.
iii) To explore 'different possible outcomes scenarios without having to experiment on
the system itself.
ADVANTAGES AND DISADVANTAGES OF POPULATION MODEL AND
SIMULATION
Learning simulation and simulation models could be used as a tool in the educational system,
where the use of particular simulation models aids in specific study, decision-making or the
path of simulation model development. There are many benefits of using simulation model
systems in education. A number of them have little to do with simulation at all, but rather with
its validity and credibility.
In abstract fields such as information science, simulation model programmes enable
users to experiment with phenomena or events, giving them a sense of reality. There are also
drawbacks, the most serious of which is the issue of legitimacy because if credibility is
questioned, there is a problem with the reality presented by the simulation model. Modeling of
the system, technology, protection, acquisition of relevant source information, key
characteristics of the behaviour, validity of results, and education are all concepts that are used
in simulation models.
Students use educational simulations to learn about various social processes, to model
real-life events and to build critical thinking skills by recognising, capturing, evaluating and

4. synthesising knowledge. Growth in a population, for example, can be simulated in various
conditions in scientific experiments. This emphasises the importance of an individual approach
to learn and demonstrates how the ability to learn at a distance combined with simulation-based
approaches and makes education accessible to everyone. Such models are extremely useful in
the teaching of simulation models, during the creation of processes and in the visualisation of
data.
Since the rules of model and simulation are based on research and historical events,
creating an entirely practical model or simulation is extremely difficult. The biggest drawback
of simulations is that they are not the exact replicas of the real thing. When compared with real-
world situations, different organisms in a population can respond differently. The advantages
of modelling and simulation include being able to test a product or system before actually
constructing it, being able to find unforeseen problems and able to speed things up or slow
them down to see progress over long or short periods of time. But nevertheless, there are some
drawbacks, such as errors in the simulation or model's programming, the cost of a simulation
model may be high, time may be required to interpret the results and people may doubt the
results' reliability. However, technologies are always updated for a more advance future, it is
beyond doubt that more complex real-life simulations can be constructed.

5. RESULT AND DISCUSSION
Systems Thinking, Experimental Learning Laboratory with Animation (STELLA®
) is one of
the modelling systems that we learnt in this course. This STELLA®
use the concepts of
simulation with the diagrams and graph being provided. So, we already choose one example
of model that being created by Billy Schoenberg, which titled “Population”. This simulation is
involving the observation of how long population could life (lifespan) based on birth rate (birth
fraction). From this graph that automatically plotted, we can see the relationship between the
lifespan and birth fraction of the population. This simulation involves two variables
(parameter), which are manipulated variable and responding variable. The manipulated
variable (X-axis) in this graph is lifespan. Meanwhile, the responding variable in this graph is
birth fraction (Y-axis). Below are the results that we get from this model.
1) P1 (No changes of parameter)
The graph above shows the normal graph to observe the relationship between the lifespan
(years) and birth fraction of the population. This graph involves no changes of parameter. From
this graph, we can see that when the lifespan increases (from 0.0 to 50.0), the birth fraction

6. decreases. It means that when a population have a lower number of offspring/children, the
population will have a high level of lifespan (can live or survive more in years).
2) P2 (parameter of stop time changes from 30 to 40)
The second graph shows the different graph result compared to the normal graph. We
adjusted the graph to investigate the relationship between these parameters. As we can see in
the graph, the lifespan being adjusted from 0.0 years- 50.0 years (in normal graph) to 0 years-
40 years (in adjusted graph 2). As lifespan is the manipulated variable (can change), so we
adjusted it. In this graph, when lifespan increases (from 0 to 40), the birth fraction also increases.
It means that when a population have a higher number of offspring/children, the population
will have a high level of lifespan (can live or survive more in years).

7. 3) P3 (Parameter of DT changes from 1/4 to 1/3)
The third graph shows the different graph result compared to adjusted graph 2. We adjusted
the graph to investigate the relationship between these parameters. As we can see in the graph,
the lifespan being adjusted from 0 years- 40 years (in adjusted graph 2) to 0 years- 50.0001
years (in adjusted graph 3). As lifespan is the manipulated variable (can change), so we adjusted
it. In this graph, when lifespan increases (from 0 to 50.0001), the birth fraction also increases.
It means that when a population have a higher number of offspring/children, the population
will have a high level of lifespan (can live or survive more in years).

8. 4) P4 (Changes of both parameter of stop time and DT)
The fourth graph shows the different graph result compared to adjusted graph 3. We
adjusted the graph to investigate the relationship between these parameters. As we can see in
the graph, the lifespan being adjusted from 0 years- 50.0001 years (in adjusted graph 3) to 0
years- 40 years (in adjusted graph 4). As lifespan is the manipulated variable (can change), so
we adjusted it. In this graph, when lifespan increases (from 0 to 40 years), the birth fraction
also increases. It means that when a population have a higher number of offspring/children, the
population will have a high level of lifespan (can live or survive more in years).

9. CONCLUSION
In conclusion, computer simulation is the used of technology devices to implement the
model or dynamic responses in an animated sequence while population known as the group of
identical organisms that live together under the same geographical areas at the same time. By
the use of population model and simulation, it allows us to analyze the concept, predict possible
outcomes, generate statistics and perform analysis regarding the population. Like the others
technologies, computer simulation also has it owns advantages and disadvantages. As for the
population model and simulation, it is very useful for the educational field as the students can
use the educational simulation to learn various social process as it allows them to model the
real-life events and increase the critical thinking among the students. Other than that, it allows
a product or system to be test before the product or system being launching. Although the
advantages of the computer simulation have been discussed, it is undeniable that it still has it
owns disadvantages. There might be errors while doing the simulation which it required higher
cost and time-consuming makes people doubting it credibility.

10. REFERENCES
Brittannica, T. Editors of Encyclopaedia (2017, April 27). Computer simulation. Encyclopedia
Britannica. https://www.britannica.com/technology/computer-simulation
Computer simulation. (n.d.). Retrieved April 17, 2021, from
https://www.britannica.com/technology/computer-simulation
Gatte, M.T., & Kadhim, R.A. (2012, October 31). Hydro power. IntechOpen - Open Science
Open Minds | IntechOpen. Retrieved April 18, 2021, from
https://www.intechopen.com/books/energy-conseervation/hydro-power
Modeling & amp; Simulation. (2011, October 09). Retrieved April 17, 2021, from https://i-
biology.net/ict-in-ib-biology/modeling-simulation/
Newsroom. (2013, February 06). ICT-Bio "computer modelling and simulation for improving
human helath". Retrieved April 17, 2021, from https://ec.europa.eu/digital-single-
market/en/news/ict-bio-computer-modelling-and-simulation-improving-human-health
Tavasuria, E., Zurida, I., & Hkied, A. (2014). The effects of 3D computer simulation on
biologystudents' achievement and memory retention. In Asia-Pasific Forum on Science
Learning and Teaching (Vol. 15, Issue 2).