What if you can get an answer to any question you want.
Then, the question you ask is going to be rather important, isn’t it?
This is the new truth in the Google and AI era. The skill we need in the workplace today is not the skill of knowing answers (knowledge – now available at your fingertips) or even finding answers (research – AI will do that for you), but rather the skill of asking right questions.
That means looking data in the face, and coming with the question that will offer most insight. That means defining the right problem in the right way so that a solution can be found. That also means not getting sidetracked by questions that don’t matter.
The same applies in Learning Design. What questions can you ask of the learners to start them on a critical thinking path? What questions will accurately evaluate their comprehension, and provide them the best opportunities to present their knowledge? What questions does your course look to answer, as you start to design it?
Here’s an example. Recently, I was developing an eLearning course for a Marketing guru – the topic was market and customer segmentation. In Marketing, it is critical to carefully segment your customers, so marketing efforts can be targeted to specific segments. Wrong segmentation can put all the marketing efforts in jeopardy. If you were designing an eLearning module to teach this skill – how will you ensure that your learners understood correctly the nuances of customer segmentation?
Which is precisely the case against the format most courses default to. Death of the multiple choice question makes it.
The best way to know if somebody knows how to do something is – to ask them to do it!
If I want to know that you know how to cycle- I am going to ask you to ride a bike and show me. Asking you to drag a pedal icon over a road image, or giving you a multiple choice asking you the various parts of a bicycle is not going to work!
We created scenario based questions to identify if learners understood segmentation. They were introduced to multiple characters, based on which the learner created customer personas and assigned these personas to the aforementioned characters. Then, they matched their fictional customers with right market segments. All along, we ensured that the learners got ample feedback that was timely, relevant but yet not overbearing.
Any standard e-Learning authoring software will allow you to create something like that. The trick is in asking the right question, which in this case is – what is the right question!
Asking learners to do the thing has a design pattern behind it: scenario-based learning, and designing engaging practice activities for the practice that goes with it.
AI finally makes open-ended questions gradable at scale. AIReady lets learners answer in their own words and get specific, criteria-based feedback back.
