# Physics IA Simulations

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PHYSICS IA SIMULATIONS
COMPUTER SIMULATIONS AS A SOURCE OF DATA
Students may use a computer simulation to generate data for their IA investigation. The
assessment criteria are the same as used for hands on, database or modelling investigations.
The only difference is that data generated within a simulation is driven by known equations,
and nothing new can be discovered. Nonetheless, the experimental skills assessed in a student
IA work can still be demonstrated: a research question can be asked, variables defined, relevant
data generated, processed and analyzed and then a conclusion and evaluation can be
established. The simulation is just another tool.
For example, the Millikan Oil Drop experiment is a classic way to determine the charge of an
electron. Although it is difficult and time consuming to perform this investigation with hands on
equipment, it is manageable and a legitimate learning exercise to use a computer simulation.
True, nothing new can be discovered this way, and the resulting precision and accuracy of the
resulting analysis may not be as high as the defined value of an electron charge, but an
understanding of the methods and the techniques can be gained. Error and uncertainties can
be addressed, and conclusions and evaluations can be made including the range of data, any
systematic assumptions, and an appreciation of the circular nature of the simulation.
SELECTING SUITABLE SIMUALITIONS
The key to using a simulation as a source of data for an experimental investigation depends on
the suitability of the simulation and what the student does with the simulation. Ask yourself if
relevant and quantitative data can be obtained in a realistic way and can the student analysis
the data in a way that addresses the assessment criteria. Some of the preliminary issues that
should be considered include:
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The research question should be scientifically interesting and complex enough to make
the study worthwhile. Students should demonstrate some creative or insightful use the
simulation through data processing, and not just play a computer game.
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Are there appropriate controllable variables? Are the variables realistic? Is there is a
reasonable range of values? Are the variables in appropriate units or can they be
converted to appropriate units? Are multiple runs of the data possible (even if identical
data are obtained)?
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Student should be aware of the circular nature of the data generated in a simulation.
Nonetheless, students can appreciate uncertainties from analogue scales and from the
least count on digital scale. Also, repeated measurements may or may not be needed,
but this needs to be confirmed. Finally, when a known quantity is established, like the
charge of an electron or the value of the gravitational constant, then comparison to the
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known value can be made keeping in mind the circular nature of the simulation data
generation.
SIMULATION TOPICS
Students have used computer simulations with the following IA topics:
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Big G by simulation
Climate prediction
Damping and SHM
Discharge of capacitor and circuit resistance
Electromagnetic Induction
Extreme condition with the general gas law
Full-wave rectification and smoothing
Gas law constant
Gravitational field and potential lines
Large amplitude pendulums
Light wavelength and intensity of internally reflected light
Millikan oil drop (charge of an electron)
Number of fins and effective heat sink
Planck’s constant
Projectile motion and air friction
Wien’s Law
Young’s double slit with light and sound
PREVIOUS PHYSICS IA EXAMPLES
The following are titles and URL links of physics student’s IA investigations that used computer
simulations:
“Angle and Resulting Wavelength in Compton Scattering”
https://kcvs.ca/details.html?key=comptonScattering
“Concentration of Salt Water and Specific Heat Capacity”
https://media.pearsoncmg.com/bc/bc_0media_chem/chem_sim/calorimetry/Calor.php
“Crater Impacts on Earth: Angle and Impact Conditions”
https://www.purdue.edu/impactearth/
“Damping of Ripple Tank Water Waves using an Applet”
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“Rate of Field Line Breaking and Induced Voltage”
“Schwarzschild Radius and Mass of Black Hole”
https://kcvs.ca/details.html?key=blackHole
PHYSICS SIMULATIONS SOURCES
Some of the popular sources of physics based computer simulations include:
IB Physics Students Simulations: A Google site with IB-like physics simulations.
KCVS: King’s University in Edmonton, Canada, web site of King’s Center for Visualization
in Science. https://kcvs.ca/cards.html?type=applets
MyPhysicsLab.com: Hundreds of physics simulations with links to tutorials for creating
interactive simulations. http://physics.bu.edu/~duffy/sims.html
On Screen Particle Physics From OnScreen Science—accurate subatomic particle decay
events simulation with analysis tools. http://www.onscreen-sci.com
oPhysics Interactive Physics Simulations listed by topic
https://ophysics.com/index.html
PhET: The University of Colorado, Boulder, USA, web site of site for Physics Education
Physics–Online.Com (for A Level and GCSE Physics and International Baccalaureate)
http://www.physics-online.com/page.cfm/courses Physics Online provides thousands
of simulations categorized by curriculum, such as the British A-Level, the American AP,
and the IB Physics courses. Under IB Physics, simulations are listed by syllabus topic
number. There is a minimal subscription fee for this service.
Physlets and HTML5 Simulations for physics from Andrew Duffy.
http://physics.bu.edu/~duffy/sims.html
The Physics Aviary. From Boston University, includes topics for AP physics courses.
https://www.thephysicsaviary.com/Physics/Programs/Labs/find.php
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Virtual Physics Labs from Kentucky Educational Television.
https://virtuallabs.ket.org/physics/
yTEACH Web Site: http://www.yteach.com/ There are hundreds of simulations that are
categorized by IB physics syllabus topics. You need to subscribe to this service.
ASSESSMENT OF A SIMULAITON IA
We act like scientific detectives when marking a student’s IA. We look for clues at various levels
of achievement as described by the indicator statements under each criterion. We start from
the ground up. Building evidence from nothing to something implies the educational philosophy
that we do not penalize the student for anything. We assess what is there, not what should or
could have been included.
Imagine an exploration that establishes the Newtonian constant of gravity, known as big G. The
student has learned how difficult the Cavendish torsional balance experiment is, and videos of
the experiment contain advanced mathematics and theory. Unable to perform such an
ambitious hands on experiment at high school level, the student considers using a computer
simulation to generate appropriate data. Relating force and distance for given masses, big G
can be determined. The student finds a simulation that can provide this data.
Under Personal Engagement we recognize the student’s justification for using a simulation over
a hands on investigation, and we witness the student’s interest in the theory of the
gravitational constant. The student explains their search and selection of an appropriate
simulation. Although this is a simple investigation the student can demonstrate authentic
involvement and the analytical skills required for an IA. The student demonstrates initiative in
appreciation the details including two possible independent variables, the expression of theory
and how to analysis the results. Some of the subtle details expressed in the students report
help established personal engagement. PE does not demand originality but rather insight and
understanding, which the student in this example demonstrates.
Under Exploration, the student can provide a clear and well-defined research question,
appreciating the variables and the assumptions. Background information can be relevant and
the methodology appropriate, given the constrains of the simulation. The student obtains data
of force, mass and distance from the simulation and graphs appropriate quantities to find big G
by the graph’s gradient. In this examples, big G is determined from a number of graphs each
with different masses. The reliability of the data will be determined by the software generating
the data, and this can be appreciated. Even if repeated measurement prove identical, this can
be stated. The sufficiently of data is limited by the range available but the number of
measurements within a range can be appreciated. There are no safety, ethical or environmental
issues here. However, generating 8 grams of greenhouse gas per Internet search was
mentioned.
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Under Analysis, the student has carefully separated out the possible independent variables and
the resulting dependent variable. Sufficient data was obtained. The simulation limited the
range to 10 meters but the student was able to make measurements every 10 centimeters for a
fixed value of the two masses. Repeated measurements were identical, so the student did not
collect repeated data. The student did repeat the process four time each with different sets of
masses. Big G was determine from a graph gradient for each of the four mass sets. The range of
masses was from 10 to 1000 kilograms. Mass measurement readings were digital. The least
count was 1 kilogram The resulting force measurement reading was also digital; here, the least
count was 1 x 10–12 Newton. There were seven significant figures in the force data. Using the
least count on digital data values as the minimum uncertainty the student determined that as a
percentage these values were insignificant. The center to center distance between masses was
measured on an analogue scale with divisions every 20 cm, but the student could easily read
one-half of a division. Normally the uncertainty would be twice the least discernable division
(for the zero and for the measure end) but the student carefully measured the center of masses
with a vertical line on the computer screen. The absolute uncertainty of any distance
measurement was tuned into a percentage and then doubled for squaring, and the same
percentage was kept for calculating the reciprocal of the square of the distance. The student
attempted to graph this uncertainty but it proved too small to see. This was explained. The data
was processed and graphed. For a given set of masses, a graph of force against the reciprocal of
the square of the distance generated a linear line with a gradient. With the known masses, the
gradient was used to derive a big G value. This was repeated four times with different mass sets
and so four (nearly identical) values of big G were determined. Final values of big G was limited
by the smallest number of significant figures used in the calculation. The impact (or the lack
thereof) of uncertainties on the graph’s gradient and resulting calculation of big G we fully
appreciated.
Under Evaluation, the student was able to describe in detail with justification a conclusion to
the research question based on the analysis of data. It is no surprise, however, that the results
are very good. The student was able to evaluate the value of big G including least count
uncertainties, and to determine big G several times each with different masses. The student
also compared their result with the most current accepted value (which has seven decimal
places of precision), and the student mentioned how different method of determine big G
result in slightly different values—an interesting ToK issue. The simulation result was within
0.1% of the textbook value. Big G is an experimental or empirical constant, not defined as the
electron or second is defined.
It is the strengths and weaknesses of the investigation that need special attention when
assessing a simulation. In this example, strengths and weaknesses might include comments
about the limited range of distances allowed in the simulation and the distance measuring
process (an analogue scale). Although the range was limited, the student made numerous
measurement within this limit. The force value precision was mostly seven digits (in a few cases
eight digits) and that was a minimal uncertainty. As a simulation, the student is not expected to
suggest improvements to the accuracy or precision of the data generated. However, because
the distance scale was an analogue, the student did mention that careful measurement on a
large computer screen would improve the recorded data precision, perhaps to one centimeter.
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Also, when the mass was large, the image of the mass was large, and this limited the possible
range by reducing the minimum separation distance by 2 to 3 meters. Fortunately, the
simulation allowed the function of “constant size” (which effectively increased the masses by
increasing their density). Overall, the simulation was based on point masses only. The student
explained this as a relevant improvement. The obvious weakness was that the simulation used
a value of G which generated the data, but the student appreciated the circular nature of the
data and was able to appreciate the overall context of the investigation. Finally, a critical
awareness of the assumptions in the method and simulation proved relevant to assessment. An
extension to this investigation might be to determine big G using a database of orbiting satellite
information, or another simulation modelling the solar system.
Under Communications, the student produced a focused and concise report (under 12 pages),
with relevant details, consistent terminology, convectional referencing, and no significant
errors or confusion in scientific terms, units or graphing expectations. Sample images of the
simulation screen were provided as well as appropriately presented data tables and graphs. The
report flowed nicely, was informative and overall was a pleasure to read.
In sum, the student’s investigation was able to address relevant assessment indicators at the
highest level. As with all student explorations, assessment requires an interpretation of the
indicators statements and judgment about what is and is not relevant.
SPECIAL ATTENTION FOR ASSESSMENT OF SIMULATION IA
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PE: The rationale for selecting a given simulation and an appreciation of what can and
cannot be determined with the simulation falls under Personal Engagement. Often,
several research question are possible using a given simulation, and therefore a student
can contribute in the design of the investigation. Often PE is revealed under the details
of method, analysis, even evaluation. PE looks for understanding and insight more than
originality.
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EX: The Exploration criterion reflects the traditional skills of any investigation. With a
simulation, the data source is a computer program, not the physical world. Students
often use Smartphone apps or datalogging to obtain data, so using a simulation is
similar (with the understanding that it is not the natural world generating the
phenomenon). Nonetheless, all the analysis skills (data collection, processing graphing,
uncertainties) can still be done with simulation data.
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EV: The Evaluation criterion needs to reflect the artificial nature and the constraints of
the simulation. Comments about the theoretical assumptions in the method as well as
scope of measurements might be made here. One cannot suggest a more precise
measurement as precision and the quality of data are defined by the simulation.
Nonetheless, strengths and weaknesses can be address accordingly by considering the
methods and results of processing the data.
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CM: Communication assessment would expect an image of the simulation included in
the report.
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