Title
Simulation integrated in teaching and learning process.
Objectives
To know what is simulation and how to use simulation during
teaching and learning process by using STELLA.
Introduction
One of the most powerful tools for analysis and
learning with a computer is simulations. A computer simulation is a
mathematical model of the significant variables and relationships of an
activity or system. Computer simulations are also define as computer-generated
dynamic models that present theoretical or simplified models of real-world
components, phenomena or processes. The variables and relationships that
represent the computer simulation system tend to be very complex. Through the
processing power of the computer the variables can be manipulated and the relationships
explored. Computer simulation can include animations, visualizations, and
interactive laboratory experiences. Computer simulations can make interactive,
authentic, meaningful learning opportunities become possible.
Learners can observe, explore, recreate, and receive
immediate feedback about real objects, phenomena, and processes that would
otherwise be too complex, time-consuming, or dangerous. Researchers studying
the use of simulations in the classroom have reported positive findings overall.
The literature indicates that simulations can be effective in developing
content knowledge and process skills, as well as in promoting more complicated
goals such as inquiry and conceptual change. In the best simulations the
systems are represented graphically so the user can see the system change. Many
of the more popular computer games are simulations. The best simulations for
education model real-life activities such as communities and businesses.
Computer simulations allow youth to control and explore complex activities.
Many activities, like flying an airplane or managing a city, are cost/time
prohibitive or even impossible for most youth.
A good simulation is the next best option to the real
activity. Teaching with computer simulations is totally different than a
traditional classroom model. Teaching with computers, especially simulations,
is maddeningly learner directed. Each learner proceeds in the direction and at
the pace best suited for them. At times, teaching with computers can be
disconcerting or bothered to someone who is most comfortable in a well
sequenced classroom. The success of computer simulations use in science education
depends on how they incorporated into curriculum and how teacher use it. The
most appropriate use of computer simulations are for a supplementary tools for
classroom instruction and laboratory. Multimedia supported, highly interactive, collaborative computer
simulations appealing growing interest because of their potentials to
supplement constructivist learning. They offer inquiry environments and
cognitive tools as a platform for learning and apply problem-solving skills.
Computer simulations are good tools to improve student’s hypothesis
construction, graphic interpretation and prediction skills. Computer
simulations also have potential for distance education laboratories.
Gains in student understanding and achievement have
been reported in general science process skills and across specific subject
areas, including physics, chemistry, biology, Earth and space science (Kulik
2002). Although conventional instructional materials such as textbooks present
two dimensional representations, simulations can offer three-dimensional manipulative
that bring the subject matter to life. Visualization results in the development
of mental constructs that allow one to think about, describe, and explain
objects, phenomena, and processes in a more true-to-life form. These are just
the habits of mind scientists rely upon in their daily work. For example, after
comparing simulated and hands-on dissection labs, Akpan and Andre (2000)
concluded, “The flexibility of these kinds of environments makes learning right
and wrong answers less important than learning to solve problems and make
decisions. Simulations promote learning about what-ifs and possibilities, not
about certainties”.
Computer simulations give students the opportunity to observe a real
world experience and interact with it. Strauss and Kinzie, 1994 state that simulations
are useful for simulating labs that are impractical, expensive, impossible, or
too dangerous to run. Due to Zietsman, 1986; Stieff, 2003, computer simulations
can contribute to conceptual change, while Sadler et al. 1999 state that
computer simulation provide open-ended experiences for students. From Mintz,
1993; White and Frederiksen, 2000; Windschitl, 2000; Dwyer & Lopez, 2001,
computer simulation provide tools for scientific inquiry and from Woodward et
al., 1988; Howse, 1998 computer simulation gives students problem solving
experiences. Computer simulations also have potentials for distance education
according to Lara & Alfonseca, 200; McIsaac and Gunawardena, 1996.
Overall, the research shows that interaction with
computer simulations resulted in measurable achievement gains and indicates that
simulations are equally, if not more, effective than traditional methods.
Access to multiple representations of phenomena, the ability to manipulate the
environment, ease of posing and testing multiple hypotheses, and ability to
control variables are consistently cited in the research as contributing to the
effectiveness of computer simulations. Other noted benefits to consider when
comparing instructional approaches include cost and time efficiency, student
enthusiasm, high engagement, and on-task behaviour while working with
simulations. Effectiveness, however, varies based on design features, support
measures, and sequencing of simulation activities within the curriculum
(Bayraktar 2002 ; Kulik 2002).
There are many examples of simulation. Some of the examples are Virtual
Planetarium Software, Virtual Optics Bench, Explore Learning Mouse Breeding and STELLA software. Virtual planetarium simulations allow students to
investigate astronomy from any perspective, from any place on Earth, at any
point in time, under the ideal conditions of a controlled environment such as
no obstructions, clouds, or fog. For example, a commercial virtual planetarium
program called Starry Night can be used in a class demonstration to answer the
question such as how do stars appear to move in the sky. Although it by no
means replaces the experience of an evening field trip to view the stars, the
student can select their own location, current date, and time to keep the
investigation authentic and meaningful to them. We and the students can make
preliminary predictions, then view and review the motion of the stars through
the sky.
We can engage students in conversation about their
observations, having them generate additional questions and revise their
predictions, then develop their own definition of circumpolar stars. By further
investigating Polaris, Ursa Major, and the Big Dipper from the equator and the
North Pole, for instance, students can notice the differences in the apparent
motion of the stars, depending on their viewpoint. Discussion with students about
all the possible explanations will leading students to understand that the
apparent movement of the stars is due to the Earth’s rotation. Through this
software, we can encourage the students to make their own observations of the
stars in the night sky from home and share their findings in class over the
course of the next week. Students should be made aware that distortions on the
edges of the Moon and other planetary bodies result from attempting to
represent a three-dimensional object on a two-dimensional computer screen.
The second example of simulation is Virtual Optics
Bench. Virtual Optics Bench is a java applet that takes instruction
using ray diagrams to a new level. We can have access to concave and convex
lenses and mirrors, point light sources, culminated light sources, and objects
for showing real and virtual images with the click of a mouse. In this dynamic
environment, students are able to visualize and investigate the effects of
changing parameters, such as the focal length of a lens or the location of a
light source. A lesson may begin with students experimenting with a variety of
lenses, noticing differences in the appearance of an image when viewed through
lenses of various curvatures. After introducing the term focal length as
a description of how curved a lens is, we as the teacher can pose a question
such as what impact does the focal length have on the position and size of the
image formed. Initial qualitative observations can be extended to a more
in-depth quantitative analysis using the Virtual Optics Bench. Although doing
so with the traditional approach of drawing ray diagrams is time-consuming and
tedious for students, this inquiry investigation is easily accomplished with
the computer simulation. Students can make and test their predictions using the
Virtual Optics Bench.
The third example is Explore Learning Mouse Breeding.
This software allow students to perform virtual genetic experiments.
Students can use this simulation to explore many questions such as can
dark-haired parents produce light-haired offspring. The Mouse Breeding
simulation is appropriate for students to use on their own in the
computer lab, individually or in small groups. Students should keep
track of their results, including parent genotypes, Punnett squares, and
phenotype ratios, in a lab notebook. Students can breed various pairs of mice,
making predictions first and then running simulated trials. Experiments might
include pairing two black-fur mice, two white-fur mice, a purebred black-fur
mouse with a purebred white-fur mouse, and two of the resulting hybrid mice.
Students will discover that the recessive white fur trait returns, and we can
discuss with them why the experimental outcomes do not always match those of
the Punnett square and direct students back to the initial question.
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Standard pattern of population when no lynx is harvest
Pattern of population for lynx and hares when number of lynx
harvest is 100
Pattern of population for lynx and hares when number of lynx
harvest is 380
Pattern of population for lynx and hares when number of lynx
harvest is 750
Discussion
There are 4 patterns of population
in this experiment. First, standard pattern of population when no lynx is
harvest. Second, pattern of population for lynx and hares when number of lynx
harvest is 100. Third, pattern of population for lynx and hares when number of
lynx harvest is 380 and lastly pattern of population for lynx and hares when
number of lynx harvest is 750. In this experiment, our predator is lynx while
our prey is hares. A predator is an animal that kills and eats other animals for food. The
animals that it eats are called prey. A prey animal is usually smaller and less
powerful than the predator that eats it. All members of a species hunt the same
animals for prey, and all prey animals have the same predators as their
enemies. Many predators live on land, but they can be found in many different
habitats, such as swamps, deserts, oceans, coral reefs, and even your home or
backyard.
A predator is also a carnivore, which means
that it eats the meat of other animals. Prey is food for other animals, but not
all food is prey. For example, many animals are herbivores, which means that
they eat only grasses and plant foods. Animals that are omnivores eat both
plants and animals. In a given territory, predators compete with each other for
the prey animals available. Prey animals are constantly aware of the
possibility of being attacked, and experience fear of the predator or signs of
its presence. Many animals are both predators and prey animals. This means that
while an animal is hunting for its food which refer to another animal, it can
become prey at any time if a larger predator attacks it. For prey, an encounter
with a predator means life or death. But for a predator, the only thing that is
at stake is a meal.
The first diagram show a standard
pattern of population for both lynx and hares when no lynx is harvest. When
there is no lynx is harvest, the population of lynx and hares are constant. The
population of lynx always higher compared to population of hares. Eventhough the population of hares is lower
than population of lynx, the number is still constant. This is due to prey
adaptations. An adaptation is a body part or
characteristic that an animal has that suits its environment and allows the
animal to survive. Second
diagram show the pattern of population for lynx and hares when number of lynx
harvest is 100. When the size of lynx harvest is 100, there is change in the
pattern of population for both prey and predators. When the population of lynx
slightly decrease by 100, the population of the prey which is hares faces a
slightly increase. When the population of predator which is lynx start to
increase back, population of hares also faced a small increases. However when
the population of lynx at the maximum number, the population of hares start to
decrease. This process continued and repeated.
Third diagram show the pattern of
population for lynx and hares when number of lynx harvest is 380. At the
beginning, we can see a slightly drastic drop of the population number for the
lynx. When this occur, we can see a slightly rapid growth in the population
number of the prey which is hares. When the number of lynx population start to
increase until it reached maximum, number of hares population start to
decrease. This is due to the maximum hunting by the lynx. The decrease of the
hares population cause a decrease in lynx population too. This happen because
lack of food available for lynx. When the lynx population start to decrease,
hares population start to increase back. The process continued and repeated.
The forth diagram show the pattern
of population for lynx and hares when number of lynx harvest is 750. Due to a
very large number of lynx harvest, the population of lynx faced a drastic drop.
This cause the population of hares to increase drastically. When the population
of hares is big, this promote an increase in lynx population due to abundance
of food supply. When the number of lynx population is maximum, the population
of hares is the minimum. This minimum population promote decreasing in lynx
population due to limited food supply. This process continued and repeated. As
we can see, this simulation show a process that took many years to happen. To
see the increasing and decreasing for both lynx and hares population, it took
about 15 years and above. By using simulation, we can make a prediction many
years ahead. This is one of the advantages of using simulation. Using
simulation in teaching and learning process will also give the same feedback.
Teacher and students will be able to make prediction when they make an
experiment by using simulation techniques.
Conclusion
As the conclusion, simulation gives
lots of benefit to teacher and student in teaching and learning process. By
using simulation, we not only can saves time but also money. Using simulation
also allow a dangerous experiment to be done. In the future, teachers should
use simulation more frequently if it is needed because simulation can help
student to increase their motivation and encourage them to make prediction in
any situation.
References
Lee Dunn, Learning and teaching briefing papers series (2002).
Access from
http://www.brookes.ac.uk/services/ocsld/resources/briefing_papers/learning_theories.pdf on November 2012.
Simulation (2012). Access from
on November 2012.
Stella (1985). Access from
on November 2012.
Dr. Sami Sahin,
Computer Simulations In Science Education: Implications for
Distance Education (2006). Access from article_12.pdf on November 2012.
Computer
Simulation Techniques : The definitive introduction! (2009). Access from
simulation.pdf on
November 2012.
Randy L. Bell and Lara K. Smetana, Using
Computer Simulations to Enhance
Science Teaching and Learning (2012). Access from tech_sec_science_chapter_3.pdf on November 2012.
Teaching and
learning with computer simulations (2012). Access from contestsimulation.pdf
on November 2012.
