My friend recently sent me a 750 GB external hard drive as a surprise gift. I was naturally excited, so I tore open the cover and packaging. Out came a shining new hard drive and a USB cord. I plugged in both ends and waited. Nothing happened. Pretty sure that I hadn’t plugged it in properly, I pulled out the cord and pushed it back into the USB slot. Nothing whatsoever. Time to go back into the packaging – I pulled out the ‘other’ cord to connect the drive to the power source. Of course. How obvious? I should have known.
I was actually taking a satisficing approach as opposed to an optimizing one.
Satisficing, according to Wikipedia, is “a decision-making strategy which attempts to meet criteria for adequacy, rather than to identify an optimal solution”. It can apply to just about any situation where you don’t evaluate your options before making a decision. Typically people satisfice when:
- They don’t have enough information
- The stakes are not too high
Now what about learners in a complex scenario? Do they really make intelligent, well thought out choices – follow the optimizing approach? Or do they just satisfice – see what comes up, and then go back and change their decision and see the response and so on?
How do you make sure learners actually follow an optimal decision making process in your interactivities? Some possibilities (each with its own pros and cons):
- Don’t allow learners to return to the previous step in a scenario
- Allow them to return, but give them negative scoring/feedback for changed decisions
- Restrict the number of times they can go back and change their decisions
What do you think is a right approach?
The learners are probably wondering what fuss is all about, and telling themselves “what do I lose by making a wrong decision, it’s a scenario after all.” And they are probably right – if even after this trial and error method, they really understand what we are trying to tell them, the learning goal is achieved, right?