Draft 14 June 2020 Dr. Mark Headlee, mheadlee@aol.com 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: • 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. • 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)? • 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 1 Draft 14 June 2020 Dr. Mark Headlee, mheadlee@aol.com 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: • • • • • • • • • • • • • • • • • • Big G by simulation Climate prediction Damping and SHM Discharge of capacitor and circuit resistance Electromagnetic Induction Extreme condition with the general gas law Faraday’s law and magnetic moment 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” https://www.falstad.com/mathphysics.html “Energy Transfer in Newton’s Cradle” https://www.myphysicslab.com/engine2D/newtons-cradle-en.html 2 Draft 14 June 2020 Dr. Mark Headlee, mheadlee@aol.com “Rate of Field Line Breaking and Induced Voltage” https://phet.colorado.edu/en/simulation/legacy/generator “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. https://sites.google.com/site/studentdcpsimulations/ 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 NASA National Aeronautics and Space Administration, undergraduate computer programs to download. https://www.grc.nasa.gov/WWW/k-12/UndergradProgs/index.htm 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 Technology. https://phet.colorado.edu/en/simulations/category/physics 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 3 Draft 14 June 2020 Dr. Mark Headlee, mheadlee@aol.com 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. 4 Draft 14 June 2020 Dr. Mark Headlee, mheadlee@aol.com 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. 5 Draft 14 June 2020 Dr. Mark Headlee, mheadlee@aol.com 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 • 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. • 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. • 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. • CM: Communication assessment would expect an image of the simulation included in the report. 6 Draft 14 June 2020 Dr. Mark Headlee, mheadlee@aol.com “Now, this is just a simulation of what the blocks will look like once they’re assembled.” https://www.bing.com/images/search?view=detailV2&ccid=Tj6wYvnH&id=DD13A7C262DC382EB6811A1BDAD1CBAFB011EA6D&thid=OIP.Tj6wYvnH_uRt9TVPVqbwJAHaFj&me diaurl=https%3a%2f%2fimage.slidesharecdn.com%2fobjectorientedcssgraemeblackwood-120901065701-phpapp01%2f95%2fobject-oriented-css-graeme-blackwood-41728.jpg%3fcb%3d1346482777&exph=546&expw=728&q=Now%2c+this+is+just+a+simulation+of+what+the+blocks+will+look+like+once+they’re+assembled.&simid=608024952 084562710&ck=89379A4B130AF6ABA42AB75C37B91059&selectedIndex=7&ajaxhist=0 7