Before we proceed, it helps to wrap a bit more definition around scenarios. We want to discuss their core structure, and some of the dimensions along with scenarios can differ.
At the core, a scenario is composed of several critical elements: a story setting, a precipitating situation, a decision with several alternative choices of action, and the consequences of those decisions – which can lead to a new situation. Underneath is a world in which the story is set and determines the outcomes of choices made by making the decision.
The story setting is the context. Within the story, some circumstance sets up the need for some choice of action. The correct answer is one option, of course (unless to make a particular point, there are no right answers). That has consequences. There are also wrong answers, with their ‘different’ consequences. One point we’ll address in more depth later is that those alternatives to the right answer shouldn’t be random or silly, but instead should represent reliable ways people go wrong.
This structure, with decision but only one choice and consequences, is also the structure of the example. There are separate nuances, however. The consequences should be explicitly conveyed, and the model should be explicitly mentioned in the decision. If it’s a wrong choice, there may need to be an explicit discussion of why the model didn’t apply in these circumstances. Introductions are different types of examples; they’re not used as a reference about how models work in context, but instead are motivating about why the coming learning is important. This essential structure can vary in distinctive ways, so it’s useful to characterize it in slightly abstract terms. The world, according to scenarios, can be in any valid state of the model – needing a decision, having made a decision – that represents the world. The transition from one state to another can be programmatic, in the case of a simulation, or simply hard-wired in other instantiations. As a consequence, there are several sub-categories of scenarios.
First, scenarios can be implemented via simulations driving the decisions. That is, an engine takes learner actions and calculates the change in the underlying model, then presents the outcome to the user for the next decision. There can be an underlying loop keeping the world moving, so you get action and learners have to respond. There’s also a check to see if terminal conditions (e.g., win/lose) have been achieved. Otherwise, the engine just keeps processing user input and determining the new situation to present to the learner.
However, the canonical form is a branching scenario. There are a range of ways these can be implemented, in practice. Similarly, there’s the simplest form, what we term a ‘mini-scenario’. Conceptually, it is worth distinguishing between the various forms, as they serve different learning needs.
For branching scenarios, as stipulated earlier, the rules about how the world works are implicitly communicated through the branches. Thus, the choices don’t trigger a recalculation of states, but instead, the choice is explicitly stated as a link to the subsequent outcome. It’s certainly a lot easier to tie together actions and consequences than to stipulate the underlying model.
There are a variety of forms for different branching scenarios. They can broaden, and narrow at particular spots, or they can be relatively shallow (not many decisions). They’re designed, however, to catch real-world relationships, so for instance, if you make a mistake with a client, you can try apologizing and see if you can get back on track.
A particular form is one I call the linear scenario. Here, no matter what choice you make, you see the consequences, but then somehow, the situation is made right before the next step. So, for instance, a boss can catch the deliverable before it goes to the client. This can be important if every learner needs to face the same problems, for instance for an assessment.
A final type of scenario is what I term a ‘mini-scenario’ (there are other terms for these, as well). Here, it’s only one decision, and you see the consequences of each answer. Really, this is just a better-written multiple-choice question, so you can use your existing tools. However, there is a caveat, you need to make sure there’s different feedback for each wrong answer (but you should be doing that anyway). Don’t forget to have consequences first!
We’ve seen scenarios executed as text adventures, graphic novel formats, audio stories, branching video, and full games. Different media have different strengths, but all are vehicles for dynamic experiences. They can also serve to illustrate examples and introductions.
Visually, images can help establish the story setting, and can convey the consequences as well. Video can provide a richer context, at the risk of more production costs. Branching video requires considerable forethought, but can yield very immersive experiences, with dynamic contexts and vivid consequences.
Text, of course, is visual, but is processed differently. You could do a scenario all in text (think the early text adventure games, such as Colossal Cave), but you can also augment with visuals, as above.
Audio alone is unusual for a scenario, but the possibilities are there. For situations where audio is the key (for example, air traffic control), having audio as at least an element is important. It can be the only channel in some instances, such as the visually impaired.
Tactile isn’t as frequently used but can be critical for learning that requires tactile experience. There are devices that receive and provide haptic feedback, so mimicking the characteristics of a surgeon’s knife or showing motion.
The real issue is the tradeoffs in costs and learning. The greater the production values required (e.g., audio and video), the greater the effort and costs. Yet, there is also increasing learning value as you move from mini-scenarios to branching scenarios. There are situations that also justify the cost of a full simulation-driven experience. The trick is knowing which to use when.
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