In developing any interactive learning environment, you will inevitable create a continuous system.
Consider interactive learning in the very traditional sense: Multiple choice creating branch points in the unfolding of a plot line or event. The decision points, outcome are controlled. This is not a continuous system though, it is discrete: true or false, A,B,C, or D: all of the above etc etc
Continuous systems involve physics and simulations. learning about trajectories or the formation of snowflakes, or how a city can reduce its net energy consumption. These types of games or more engaging because their replay value is higher. The outcome of any game can have effectively unlimited possibilities.
“Interactive games are taking their place on major policy websites.”
The downside is that parameters must be tuned. In a physics simulation, this may be straightforward – a trajectory of a cannon ball is determined by air resistance and gravity
But what about “The Carbon Neutral City” ?
Interactive games are taking their place on major policy websites. These games, by virtue of their attraction, novelty, and portability (redeployed throughout the web) represent much of the message in a policy website. Take, for example, a game in which the user attempts to make a city have a carbon neutral footprint. They can install wind turbines, or mandate ethanol fuel for vehicles. The game/simulation designer simply factors the user’s changes into a net Carbon tally variable – but how do you assign something as controversial as “net” environmental impact in an open system?
This is where the policy of the client may unintentionally (or intentionally) show up as ‘fact’ within the simulation. Suppose you reuse the same simulation for three different clients – an agricultural client that wants to show the environmental advantage of Corn Ethanol, a producer of wind turbines, and a nuclear energy provider. Would the parameters associated with, say, installing wind turbines or re-tasking farmland to corn Ethanol production change?