I liked how relevant this paper was to our discussions/in class activities for the past few weeks. One part that stood out to me was the aggregate-behavior model. When we were designing a starlogonova model, our group decided to model swarming fish along with their interactions with sharks and fish trawls. In order to best approximate fish swarming behavior, we attempted to use the aggregate behavior model by programming each fish agent to swim close to but not touching other fish of the same species. This individually programmed behavior manifested itself in small schools of fish that would swim around pretty synchronously. This sort of activity could be pretty easily modeled using students as agents. By giving them a specific set of instructions on how to react when interacting with another student, emergent behaviors could be predicted and studied. The benefit of this game is that it requires only the students themselves and a reasonably open space.
In my thermodynamics class, we're going through statistical thermodynamics. Basically, you look at the probability of a given molecule existing in a given energy level, then by figuring out the probability distribution over all energy levels and using that distribution, you could extrapolate some of the macroscopic properties of the system. This seems a little bit harder to examine using ADI given the complex and somewhat abstract nature of statistical thermodynamics, but would be worth considering using as an additional teaching tool.
Another possible analysis method I could use in ADI is cause and effect. I could have students look examine different reactions under different conditions and examine how fast the reactions occur. This would require ways to analyze reaction speeds (probably visually or thermometrically), ways to change experimental conditions (pressure chamber, hot plate, mortar and pestle for surface area dependent reactions), and obviously the chemical reactants and hardware necessary to carry out the reactions.