In a comment to my post on Managing the Complexity in Branching Scenarios, Nicole Legault made a interesting point. “Why make a learner go so far down a wrong path? I think it’s best to correct and try to get them back on the right (or best) path.”
To some extent, I agree with Nicole. I’m not sure how much value there is to learners in going down seven steps of the wrong path with no way to recover. Where I perhaps disagree is about how the correction should happen. I try to give learners the opportunity to correct their own mistakes. However, that’s different from correcting them and forcing them back on the right path.
There are a couple of ways to handle wrong answers in scenarios.
One way is limited branching. Instead of a true branching scenario with multiple endings, this is essentially a single correct path and a single ending. When you make an incorrect choice, you get some customized feedback and perhaps see limited consequences of your decision. In the long run, there are no real consequences for mistakes. You are forced back to the correct path, regardless of your mistakes.
Although he doesn’t call it that, limited branching is the model explained in Tom Kuhlmann’s Rapid eLearning Blog as an easy way to build scenarios. Tom points out that this model is simpler doesn’t get you overly bogged down in complexity.
In limited branching, you can get the wrong answer every single time, and the scenario still propels you forward. This works OK if your scenario is a series of independent decisions rather than multiple decisions in a single large scenario. If you’re teaching a process with multiple steps, where each step is contingent on the previous step, this method doesn’t create as realistic of an assessment.
Immediate Failure and Restart
The opposite end of the spectrum from limited branching (where you can make endless wrong answers) is immediate, catastrophic failure. If you make a single incorrect decision, you restart the scenario back at the beginning. Personally, I don’t like scenarios where a single wrong answer results in catastrophic failure unless that’s what would happen in real life. Some errors really are major and should result in immediate restarts.
If you’re creating training for nurses, administering 10 times the needed dose of a medication is a catastrophic failure. If you’re creating a scenario to show what to do in an active shooter situation, a decision that results in someone dying is a catastrophic failure. In both of those scenarios, forcing learners to restart at the beginning is appropriate.
2 or 3 Consecutive Wrong Answers
Most of the time in scenarios, we’re working with gray area. In real life, we often have opportunities to change paths and correct mistakes. Where a single isolated mistake can be corrected, the cumulative effect of several wrong answers is the real concern.
In my scenarios, I usually try to limit it to two or three consecutive wrong answers before a restart. I give people opportunities to get back on the right path by making better choices. If they keep going down the wrong path, they have to restart and try again. I won’t force them to correct; learners need the opportunity to fail.
In this example, there are good (green), OK (orange), and bad (red) choices. If you choose C (red) at the beginning, you may reach a poor ending after just 2 choices. However, if you improve your choices, you can get back to a good (green) choice by correcting your mistakes.
Limiting it to two or three consecutive wrong answers also helps limit the complexity of branching scenarios. You don’t have to create a full-length path of increasingly wrong answers.
Giving people a short, but incorrect (or partially incorrect), path also gives you the opportunity to show delayed consequences.
What do you do?
How do you handle wrong answers in a branching scenario? How long do you let learners go down an incorrect path before either forcing a restart or forcing them back on the correct path?