Simulations have a lot of potential for both lab skill development, demonstrations of concepts and for potential IA work. The specific sections on physics, biology and chemistry will have a curated list of simulations that may be appropriate for learning and/or IA work.
However, there are some important considerations:
1. IA simulations should not be ‘animations’ (which produce the same results each time) or random animations (which yield random results).
2. The simulation must have a decent number of variables. Figure one and two show two different natural selection simulations using peppered moths. Figure one is good for having students explore the concept of natural selection as applied to peppered moths, but would not be appropriate as an IA as there are very limited variables that can be changed. Figure two, however, could be used as potential IA as the student can change a larger number of variables such as selection pressure, pollution amounts and starting numbers of moths.
Figure 1: A peppered moth simulation from Arizona State University
Figure 2: A peppered moth simulation from Netlogo (Wilensky, U. (1997). NetLogo Peppered Moths model.)
From our experience, here are the most common mistakes made with simulations:
A lack of justification for the variables. Students need to justify why they chose the variables they did. Netlogo, a highly recommended free simulation software, has an info section that the students can use to justify their choice of variables.
Simulations that get the same answer every time and not mentioning this feature in their IA. In this case, the student needs to mention that the simulation generates the same data every time in their IA. This could be an evaluation point later about reliability. Simulations that generate the same value every time are probably not the most appropriate simulation to use for an IA as students may have trouble reaching the higher mark bands. The key point to remember is that methodology looks at what the student does with the data, not the source of the data. Netlogo, for example, has randomness built in so that students are not getting the same answer each time.
Simulations are not compared to 'real-life data'. This is a powerful technique for writing robust conclusions and evaluations in simulation IAs. For example:
If you are using a simulation to look at a factor affecting the spread of HIV, comparing the database results to real-life data about how that factor affects the spread of HIV shows both personal engagement and a connection to scientific literature.
If you were using 3D modelling software to look at the shape of molecules and bond angles, and you noticed that the simulation did not take into account the effect of lone pairs of electrons meaning an incorrect bond angle is given.
If you were investigating projectile motion and the simulation did not include air resistance and how the drag force increases with speed or surface area you could use the relevant formulas to show how these variables would have affected the results.