The key to adaptive design and personalization is user choice. This is true whether an AI system is generating suggestions or if the Learner Intelligence (LI) adaptive model is used. One of the core tenants of LI is that given a choice, the learner will always choose how he or she learns best.
There are many ways to provide learning choices in an online course. Pre-tests and knowledge checks are typically used sparingly and thus only provide a limited degree of adaptability and personalization. The same can be said for branching. Branching is typically used to change what comes next, which is then followed by a period of linear content.
The LI model uses the branching concept in a different way. Instead of branching to linear content, which would be an absolute change, LI style branching is relative. Rather than change what comes next, the change relates to what is currently being viewed. For example, if I am learning how to connect a power supply, I may have the option to choose from a technical, sales, or consumer context. Each choice would explain connecting the power supply from a different perspective.
The second distinguishing characteristic of LI style branching is that the options to choose are repeatable. Having selected a choice, the learner can choose again or switch back to an earlier choice. This opens the possibility of viewing content from multiple perspectives, even all perspectives. In the previous example, the learner could choose to see how to connect the power supply from all three perspectives. Imagine how powerful this can be in your training if learners studied a concept repeatedly from different angles.
The LI approach to branching is also recurring. As he learner advances through the course the same branching options are always present. That means the user can switch perspectives at any point in the course and in any order. Using the same example, the three perspectives would be available as the user advanced to turning the power on, testing the system, and powering down.
Adaptive Navigation – Relative, Repeatable, Recurring
We now understand what makes adaptive navigation so powerful in generating personalized learning. Adaptive navigation is relative, repeatable, and recurring. Multiple choices at any time provides total fluidity in selecting unique learning paths. How will you apply this in your training courses? This learner centric approach to training provides a certain level of self-directed learning. Next week I will dive deeper into The Benefits of Learner Choice.