Page 141 - Getting the Picture Modeling and Simulation in Secondary Computer Science Education

Students’ Understanding and Difficulties
some of the agents. Consequently, some students were content with what they observed, while others realized their models needed to be improved.
Some students observed model’s behavior, established event validity and interpreted the model’s outcomes to be in line with reality and thus acceptable. For example, student S2, who modeled potato farming, said, “And that is what we looked at [...] that a plant doesn’t get sick at a random spot, but that it really gets infected [by the neighbors]”. Student S11 traced the behavior of the individual agents waiting in the queue as well as observed the lengths of the queues through animation and also concluded that the model’s behavior was realistic enough.
Other students encountered what they experienced as unrealistic behavior of their models. We observed twofold approach to dealing with this phenomenon: either fixing the problem or deciding that the problem is not relevant. Student S8, who modeled Ohm’s law, saw an error concerning electrons not bouncing back when colliding with atoms, and fixed it, as described in the section on verification.
Group G2, who modeled the evacuation of a burning building, partly fixed the problem, as described in the section on abstraction (section 6.4.1). After fixing the problem, they observed that people in their model left the building quickly enough, so they decided that their model was nevertheless realistic enough.
Two groups did not employ visual inspection: in the cheese barn model made
by group 5, visual aspects such as position, movement or interactions of cheeses
play no role and cheeses only change color to indicate their age. Similarly, group 6 G3 modeled no visual interactions in their Mars colony model and therefore relied
on observation of quantitative measures to validate their model.
Quantitative Measures. Almost all the students observe the outcomes of their models through quantitative measures. To this end, they observe relevant numerical values produced by the model by keeping an eye on monitors23 to observe performance measures, or charts produced while the model runs to observe operational graphics — and thus interpret the model’s outcomes subjectively. In their report, group G1 who modeled potato farming, said, “We tested these things extensively by using monitors.” Student S5, who modeled sustainability of human colony on Mars, said,” we got a very oscillating line of the number of people on the planet, say, because there were a lot of people being born and, say, if there was enough food, then the potatoes were gone, then everyone died.” Student S10, who modeled cheese barn, said, “... so you can never have more than 144, [...]
23 A monitor in NetLogo is an interface element displaying the value of a variable.
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